Manuel Abreu, Luís Garrote, Luís Conde Bento,
Bradley Schmerl, Urbano J Nunes and
David Garlan. Formal Verification of Deep Neural Networks for Classification in Industrial Environments. In Proc. the 16th International Conference on Information Science and Technology, Coimbra, Portugal. October 1-6, 2026.
Abstract: Deep Learning methods, particularly Convolutional Neural Networks (CNN), have become widely adopted in indus- trial computer vision applications due to their strong image classification capabilities. In industrial environments, reliable object recognition is essential for automated systems such as warehouse automation and sorting processes, where incorrect classifications may lead to operational failures and reduced system reliability. Despite their performance, CNN-based models remain vulnerable to adversarial perturbations, raising concerns regarding their deployment in safety-critical applications. Industrial vision systems deployed in safety-critical environ- ments must operate reliably under adversarial perturbations and common perception degradations. This work presents a robustness analysis of image classification models for industrial environments using RGB and RGB-D data. An industrial object dataset containing seven classes was constructed through an ob- ject detection and extraction pipeline, resulting in object-centered image samples. Two CNN-based architectures were developed and evaluated: a baseline RGB model and a multimodal RGB-D model. Model robustness was analyzed through both formal verifica- tion and corruption-based robustness evaluation. Formal verifi- cation was performed using the alpha-beta-CROWN framework under different perturbation magnitudes, assessing whether the CNNs preserve their predictions under bounded perturbations. In addition, corruption-based experiments following the ImageNet- C protocol were conducted to evaluate model behavior under common image degradations.
Paulo Canelas,
Bradley Schmerl, Alcides Fonesca and Christopher Steven Timperley. How does ROS-based Robot Software Evolve? An Empirical Study on Architectural Evolution. In Proc. the 20th European Conference on Software Architecture (ECSA 2026), Bolzano, Italy. September 7-11, 2026.
https://doi.org/10.7551/mitpress/2705.003.0004.
To Appear.
Abstract: The Robot Operating System (ROS) is the de facto framework for building open-source robotic software systems. Developers architect their system by using reusable components, connecting them via config- uration files and source code. These architectures evolve as developers add, remove, and modify components, connections, and configurations. These changes can lead to architectural misconfigurations detectable by specification languages and analysis tools. However, these focus on snapshots in time without considering how architectures evolve. By un- derstanding this evolution, we can help specification writers estimate the maintenance burden of their descriptions and promote tools toward the most change-prone elements. To that end, we conduct an empirical study of architectural evolution in open-source ROS systems. We develop a differential, cross-language architectural recovery tool that extracts components, connections, and configurations from source code and apply it to 294 repositories, with 1728 packages across 9914 releases, spanning ROS 1 and ROS 2 in C/C++, Python, and XML. As ROS repositories aggregate multiple packages at different maturity stages, we analyze evolution at the repository and package levels. We find that evolution di- verges across levels, with repositories remaining addition-dominant while mature packages consolidate by removing connections and configurations. Furthermore, changes co-occur across elements: component removals are associated with the removal of their connections and configurations.
Abstract: Software systems are composed of interacting processes that share data between each other in order to satisfy system properties. When these processes deviate and are unavailable to transmit data—due to events such as software bugs, hardware failures, or security attacks—a resilient system will continue delivering safety-critical services. Identify- ing the processes required to satisfy system properties is not a trivial task as there may exist multiple alternative dataflow paths that each satisfy the property. In this work, we propose a formal modeling and analysis technique to compute the sets of minimal processes required to satisfy dataflow properties. The computation of these sets is reduced to a maximum satisfiability problem via modeling in AlloyMax, a for- mal modeling language that performs bounded model checking. We then present a method to formally define the resilience criteria for a system by constraining the minimal dataflow required under maximum system deviations. The efficacy of this work is then evaluated with four case studies motivated from real-world systems with promising results.
Paulo Canelas,
Bradley Schmerl, Alcides Fonesca and Christopher Steven Timperley. ROSpec: A Domain-Specific Language for ROS-based Robot Software. In Proc. ACM Programming Languages 9, OOPSLA2, Singapore. p. 29. October 12-18, 2025.
https://doi.org/10.1145/3763169.
Abstract: Component-based robot software frameworks, such as the Robot Operating System (ROS), allow developers to quickly compose and execute systems by focusing on configuring and integrating reusable, off-the-shelf components. However, these components often lack documentation on how to configure and integrate them correctly. Even when documentation exists, its natural language specifications are not enforced, resulting in misconfigurations that lead to unpredictable and potentially dangerous robot behaviors. In this work, we introduce ROSpec, a ROS-tailored domain-specific language designed to specify and verify component configurations and their integration. ROSpec’s design is grounded in ROS domain concepts and informed by a prior empirical study on misconfigurations, allowing the language to provide a usable and expressive way of specifying and detecting misconfigurations. At a high level, ROSpec verifies the correctness of argument and component configurations, ensures the correct integration of components by checking their communication properties, and checks if configurations respect the assumptions and constraints of their deployment context. We demonstrate ROSpec’s ability to specify and verify components by modeling a medium-sized warehouse robot with 19 components, and by manually analyzing, categorizing, and implementing partial specifications for components from a dataset of 182 misconfiguration questions extracted from a robotics Q&A platform.
Paulo Canelas,
Bradley Schmerl, Alcides Fonesca and Christopher Steven Timperley. The Usability Argument for ROS-based Robot Architectural Description Languages. In Proc. the 15th annual workshop on the intersection of HCI and PL. February 17-18, 2025.
Abstract: Component-based architectures in robotics, like the Robot Operating System (ROS), simplify system development by integrating reusable, off-the-shelf components. However, these components often lack sufficient documentation on their use and the architectures they form, leading to architectural misconfigurations. General-purpose Architectural Description Languages (ADLs) effectively provide properties to detect misconfigurations but are burdensome to use, as they require mapping new language-specific concepts to the ROS domain. This work introduces rospec, a novel ADL embedding ROS-specific knowledge to help developers specify and detect architectural misconfigurations. By mapping the language's syntax with ROS concepts, we argue that practitioners can more easily understand and reason about specifications written in the language. We provide an example and compare it with a general-purpose ADL.
Paulo Canelas,
Bradley Schmerl, Alcides Fonesca and Christopher Steven Timperley. Understanding Misconfigurations in ROS: An Empirical Studyand Current Approaches. In The ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA), Vienna, Austria. September 16-20, 2024.
https://doi.org/10.1145/3650212.3680350.
Abstract: The Robot Operating System (ROS) is a highly popular framework and ecosystem that allows developers to build robot software systems from reusable, off-the-shelf components. Systems are often built by customizing and connecting components into a working ensemble via configuration files. While reusable components allow developers to quickly prototype working robots in theory, in practice, ensuring that those components are configured and connected appropriately is fundamentally challenging, as evidenced by the large number of questions asked on developer forums. Developers must be careful to abide by the assumptions of individual components, which are often unchecked and unstated when building their systems. Failure to do so can result in misconfigurations that are only discovered once the robot is deployed in the field, at which point errors may lead to unpredictable and dangerous behavior. Despite misconfigurations having been studied in the broader context of software engineering, robotics software (and ROS in particular) poses domain-specific challenges with potentially disastrous consequences. To understand and improve the reliability of ROS projects, it is critical to identify the types of misconfigurations developers face. To that end, we study ROS Answers, a Q&A platform, to identify and categorize misconfigurations during ROS development. We then conduct a literature review to assess the coverage of these misconfigurations using existing detection techniques. In total, we find 12 high-level categories and 50 sub-categories of misconfigurations. Of these categories, 27 are not covered by existing techniques. To conclude, we discuss how to tackle those misconfigurations in future work.
Abstract: The emergence of powerful automated design tools in many domains is changing the nature of design, as human-intensive activities can be increasingly off-loaded to those tools. Rather than having a human consider only handful of options, as has been done historically, such tools now enable the generation of a large space of potential designs, exhibiting different tradeoffs among competing qualities of merit, and supporting systematic exploration of the design space. At the same time, this paradigm raises new challenges centered on enabling humans to effectively navigate that generated space in order to select a design that best meets their requirements. In this paper we describe our experience in the domain of generative manufacturing, in which we developed a novel design environment for airplane parts manufacturing that incorporates a number of sophisticated design tools and attempts to tackle the emergent problems of design space exploration that are faced by designers of those parts. We use this experience to highlight the challenges that we faced and reflect on their applicability more generally to tool-assisted software design environments.
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Abstract: Microservice-based architectures consist of numer- ous, loosely coupled services with multiple instances. Service meshes aim to simplify traffic management and prevent mi- croservice overload through circuit breaking and request retry mechanisms. Previous studies have demonstrated that the static configuration of these mechanisms is unfit for the dynamic environment of microservices. We conduct a sensitivity analysis to understand the impact of retrying across a wide range of scenarios. Based on the findings, we propose a retry controller that can also work with dynamically configured circuit breakers. We have empirically assessed our proposed controller in various scenarios, including transient overload and noisy neighbors while enforcing adaptive circuit breaking. The results show that our proposed controller does not deviate from a well-tuned configura- tion while maintaining carried response time and adapting to the changes. In comparison to the default static retry configuration that is mostly used in practice, our approach improves the carried throughput up to 12x and 32x respectively in the cases of transient overload and noisy neighbors.
Abstract: Robot systems are growing in importance and complexity. Ecosystems for robot software, such as the ROS, provide libraries of reusable software components that can be configured and composed into larger systems. To support compositionality, ROS uses late binding and architecture configuration via "launch files" that describe how to initialize the components in a system.
However, late binding often leads to systems failing silently due to misconfiguration, for example by misrouting or dropping messages entirely.
In this paper we present ROSDiscover, which statically recovers the run-time architecture of ROS systems to find such architecture misconfiguration bugs. First, ROSDiscover constructs component level architectural models (ports, parameters) from source code. Second, architecture configuration files are analyzed to compose the system from these component models and derive the connections in the system. Finally, the reconstructed architecture is checked against architectural rules described in first-order logic to identify potential misconfigurations.
We present an evaluation of ROSDiscover on real world, off-the-shelf robotic systems, measuring the accuracy, effectiveness, and practicality of our approach. To that end, we collected the first data set of architecture configuration bugs in ROS from popular open-source systems and measure how effective our approach is for detecting configuration bugs in that set.
Danny Weyns, Tomas Bures, Radu Calinescu, Barnaby Craggs, John Fitzgerald,
David Garlan, Bashar Nuseibeh, Liliana Pasquale, Awais Rashid,
Ivan Ruchkin and
Bradley Schmerl. Six Software Engineering Principles for Smarter Cyber-Physical Systems. In Proceedings of the Workshop on Self-Improving System Integration. September 27, 2021.
https://doi.org/10.1109/acsos-c52956.2021.00058.
Abstract: Cyber-Physical Systems (CPS) integrate computational and physical components. With the digitisation of society and industry and the progressing integration of systems, CPS need to become 'smarter' - in the sense that they can adapt and learn to handle new and unexpected conditions, and improve over time. Smarter CPS present a combination of challenges that existing engineering methods have difficulties addressing: inter-twined digital, physical and social spaces, need for heterogeneous modelling formalisms, demand for context-tied cooperation to achieve system goals, widespread uncertainty and disruptions in changing contexts, inherent human constituents, and continuous encounter with new situations. While approaches have been put forward to deal with some of these challenges, a coherent perspective on engineering smarter CPS is lacking. In this paper, we present six engineering principles for addressing the challenges of smarter CPS. As smarter CPS are software-intensive systems, we approach them from a software engineering perspective with the angle of self-adaptation that offers an effective approach to deal with run-time change. The six principles create an integrated landscape for the engineering and operation of smarter CPS.
Abstract: In software design, guaranteeing the correctness of run-time system behavior while achieving an acceptable balance among multiple quality attributes remains a challenging problem.
Moreover, providing guarantees about the satisfaction of those requirements when systems are subject to uncertain environments is even more challenging.
While recent developments in architectural analysis techniques can assist architects in exploring the satisfaction of quantitative guarantees across the design space, existing approaches are still limited because they do not explicitly link design decisions to satisfaction of quality requirements.
Furthermore, the amount of information they yield can be overwhelming to a human designer, making it difficult to distinguish the forest through the trees.
In this paper, we present an approach to analyzing architectural design spaces that addresses these limitations and provides a basis to enable the explainability of design tradeoffs.
Our approach combines dimensionality reduction techniques employed in machine learning pipelines with quantitative verification to enable architects to understand how design decisions contribute to the satisfaction of strict quantitative guarantees under uncertainty across the design space.
Our results show feasibility of the approach in two case studies and evidence that dimensionality reduction is a viable approach to facilitate comprehension of tradeoffs in poorly-understood design spaces.
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Abstract: Many self-adaptive systems benefit from human involvement, where human operators can complement the capabilities of systems (e.g., by supervising decisions, or performing adaptations and tasks involving physical changes that cannot be automated). However, insufficient preparation (e.g., lack of task context comprehension) may hinder the effectiveness of human involvement, especially when operators are unexpectedly interrupted to perform a new task. Preparatory notification of a task provided in advance can sometimes help human operators focus their attention on the forthcoming task and understand its context before task execution, hence improving effectiveness. Nevertheless, deciding when to use preparatory notification as a tactic is not obvious and entails considering different factors that include uncertainties induced by human operator behavior (who might ignore the notice message), human attributes (e.g., operator training level), and other information that refers to the state of the system and its environment.
In this paper, informed by work in cognitive science on human attention and context management, we introduce a formal framework to reason about
the usage of preparatory notifications
in self-adaptive systems involving human operators. Our framework characterizes the effects of managing attention via task notification in terms of task context comprehension. We also build on our framework to develop an automated probabilistic reasoning technique able to determine when and in what form a preparatory notification tactic should be used to optimize system goals. We illustrate our approach in a representative scenario of human-robot collaborative goods delivery.
Marin Litoiu, Necmiye Ozay, Colin Paterson, Kenji Tei, Alberto Leva, Masako Kishida,
Bradley Schmerl and Danny Weyns. Towards Better Adaptive Systems by Combining MAPE, Control Theory, and Machine Learning. In Proceedings of the 16th Symposium on Software Engineering for Adaptive and Self-Managing Systems, Virtual. May 17-24, 2021.
https://doi.org/10.1109/seams51251.2021.00036.
Abstract: Two established techniques to engineer adaptive systems are architecture-based adaptation that uses a Monitor-Analysis-Planning-Executing (MAPE) loop that reasons over architectural models (aka Knowledge) to make adaptation decisions, and control-based adaptation that relies on principles of control theory (CT) to realize adaptation.
Recently, we also observe a rapid growing interest in applying machine learning (ML) to support different adaptation mechanisms.
While MAPE and CT have particular characteristics and strengths to be applied independently, in this paper we are concerned with the question of how these techniques are related with one another and whether combining them and supporting them with ML can produce better adaptive systems.
We motivate the combined use of different adaptation techniques using scenarios from two different domains and illustrate the analysis involved in combining different adaptation techniques. The paper concludes with suggestions for further research in this interesting area.
Abstract: Many applications have taken advantage of cloud
provided autonomic capabilities, commonly auto-scaling, to harness
easily available compute capacity to maintain performance
against defined quality objectives. This has caused the management
complexity of enterprise applications to increase. It is
now common for an application to be a collection of autonomic
sub-systems. However, combining individual autonomic systems
to create an application can lead to behaviors that negatively
impact the global aggregate utility of the application and in
some cases can be conflicting and self-destructive. Commonly,
human administrators address these behaviors as part of a design
time analysis of the situation or a run time mitigation of the
undesired effects. However, the task of controlling and mitigating
undesirable behaviors is complex and error prone. To handle the
complexity of managing a collection of autonomic systems we
have previously proposed an automated approach to the creation
of a higher level autonomic management system, referred to as a
Meta-Manager. In this paper, we improve upon prior work with
a more streamlined and understandable formal representation of
the approach, expand its capabilities to include global knowledge,
and test its potential applicability and effectiveness by managing
the complexity of a collection of autonomic systems in a case
study of a major outage suffered by the Google Cloud Platform.
Abstract: Self-adaptive software systems rely on planning to
make adaptation decisions autonomously. Planning is required
to produce high-quality adaptation plans in a timely manner;
however, quality and timeliness of planning are conflicting in
nature. This conflict can be reconciled with hybrid planning,
which can combine reactive planning (to quickly provide an
emergency response) with deliberative planning that take time
but determine a higher-quality plan. While often effective, reactive
planning sometimes risks making the situation worse. Hence,
a challenge in hybrid planning is to decide whether to invoke
reactive planning until the deliberative planning is ready with a
high-quality plan. To make this decision, this paper proposes a
novel learning-based approach. We demonstrate that this learning-based
approach outperforms existing techniques that are based
on specifying fixed conditions to invoke reactive planning in two
domains: enterprise cloud systems and unmanned aerial vehicles.
Martin Pfannmüller, Martin Breitbach, Christian Krupitzer, Marcus Weckesser, Christian Becker,
Bradley Schmerl and Andy Schürr. REACT: A Model-Based Runtime Environment for Adapting Communication Systems. In Proceedings of the 2020 IEEE Conference on Autonomic Computing and Self-organizing Systems (ACSOS), Washington, D.C. August 19-23, 2020.
https://doi.org/10.1109/acsos49614.2020.00027.
Abstract: Trends such as the Internet of Things or edge computing lead to a growing number of networked devices. Hence, it is becoming increasingly important to manage communication systems at runtime. Adding self-adaptive capabilities is one approach to reduce administrative effort and cope with changing execution contexts. Existing frameworks for building self-adaptive software can help to reduce development effort in general. Yet, they are neither tailored towards the use in communication systems nor easily usable without profound knowledge in self-adaptive systems development. In this paper, we propose REACT, a reusable, model-based runtime environment to complement communication systems with adaptive behavior. It addresses the heterogeneity and distribution aspects of networks and reduces development effort. REACT empowers developers of communication systems to add adaptive behavior without having experience in self-adaptive systems development. We show the effectiveness and efficiency of our prototype in an experimental evaluation based on two distinct use cases from the communication systems domain: cloud resource management and software-defined networking. The first use case includes a comparison with Rainbow, which represents a state-of-the-art model-based framework for building self-adaptive systems. The second use case applies REACT in a sophisticated, real-world communication system scenario.
Abstract: Many self-adaptive systems benefit from human involvement, where a human operator can provide expertise not available to the system and perform adaptations involving physical changes that cannot be automated.
However, a lack of transparency and intelligibility of system goals and the autonomous behaviors enacted to achieve them may hinder a human operator's effort to make such involvement effective.
Explanation is sometimes helpful to allow the human to understand why the system is making certain decisions. However, explanations come with costs in terms of, e.g., delayed actions.
Hence, it is not always obvious whether explanations will improve the satisfaction of system goals and, if so,
when to provide them to the operator. In this work, we define a formal framework for reasoning about explanations of adaptive system behaviors and the conditions under which they are warranted. Specifically, we characterize explanations in terms of their impact on a human operator's ability to effectively engage in adaptive actions.
We then present
a decision-making approach for planning in self-adaptation
that leverages a probabilistic reasoning tool to determine when the explanation should be used
in an adaptation strategy in order to improve overall system utility.
We illustrate our approach in a representative scenario for the application of an adaptive news website in the context of potential denial-of-service attacks.
Abstract: Self-adaptive systems increasingly need to reason about and adapt both structural and behavioral system aspects, such as in mobile service robots, which must reason about missions that they need to achieve and the architecture of the software executing them. Deciding how to best adapt these systems to run time changes is challenging because it entails considering mutual dependencies between the software architecture that the system is running and the outcome of plans for completing tasks, while also considering multiple trade-offs and uncertainties. Considering all these aspects in planning for adaptation often yields large solution spaces which cannot be adequately explored at run time.
We address this challenge by proposing a planning approach able to consider the impact of mutual dependencies between software architecture and task planning on the satisfaction of mission goals. The approach is able to reason quantitatively about the outcome of adaptation decisions handling both the reconfiguration of the system's architecture and adaptation of task plans under uncertainty and in a rich trade-off space.
Our results show: (i) feasibility of run-time decision-making for self-adaptation in an otherwise intractable solution space by dividing-and-conquering adaptation into architecture reconfiguration and task planning sub-problems, and (ii) improved quality of adaptation decisions with respect to decision making that does not consider dependencies between architecture and task planning.
Sheila Venero,
Bradley Schmerl, Leonardo Montecchi, Julio Cesar Dos Reis and Cecilia M. F. Rubira. Automated Planning for supporting Knowledge-intensive Processes. In Proceedings of the 20th Business Process Modeling, Development and Support (BPMDS) Working Conference. June 8-9, 2020.
https://doi.org/10.1007/978-3-030-49418-6_7.
Abstract: Knowledge-intensive processes (KiPs) are processes characterized by high levels of unpredictability and dynamism. Their process structure may not be known before their execution. One way to cope with this uncertainty is to defer decisions regarding the process structure until run time. In this paper, we consider the definition of the process structure as a planning problem. Our approach uses automated planning techniques to generate plans that define process models according to the current context. The generated plan model relies on a metamodel called METAKIP that represents the basic elements of KiPs.
Our solution explores Markov Decision Processes (MDP) to generate plan models. This technique allows uncertainty representation by defining state transition probabilities, which gives us more flexibility than traditional approaches. We construct an MDP model and solve it with the help of the PRISM model-checker. The solution is evaluated by means of a proof of concept in the medical domain and reveals the feasibility of our approach.
Ivano Malavolta, Grace A. Lewis,
Bradley Schmerl, Patricia Lago and
David Garlan. How do you Architect your Robots? State of the Practice and Guidelines for ROS-based System. In Proceedings of the 42nd International Conference on Software Engineering: Software Engineering in Practice. May 23-29, 2020.
https://doi.org/10.1145/3377813.3381358.
Abstract: The Robot Operating System (ROS) is the de-facto standard for robotic software.
If on one hand ROS is helping roboticists, e.g., by providing a standardized communication platform, on the other hand ROS-based systems are getting larger and more complex and could benefit from good software architecture practices.
This paper presents an observational study aimed at (i) unveiling the state of the practice for architecture of ROS-based systems and (ii) providing guidance to roboticists about how to properly architect ROS-based systems. To achieve these goals, we (i) build a dataset of 335 GitHub repositories containing real open-source ROS-based systems, (ii) mine the repositories for extracting the state of the practice about how roboticists are architecting them, and (iii) synthesize a catalog of 49 evidence-based guidelines for architecting ROS-based systems.
The guidelines have been validated by 77 roboticists working on real-world open-source ROS-based systems.
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Abstract: This paper presents Kubow, an extensible architecture-based selfadaptation
service for cloud native applications. Kubow itself was
implemented by customizing and extending the Rainbow self-adaptation
framework with support for Docker containers and Kubernetes.
The paper highlights Kubow’s architecture and main design
decisions, and illustrates its use and configuration through a simple
example. An accompanying demo video is available at the project’s
web site: https://ppgia-unifor.github.io/kubow/.
Abstract: Modern cyber-physical systems (e.g., robotics systems) are typically composed of physical and software components, the characteristics of which are likely to change over time.
Assumptions about parts of the system made at design time may not hold at run time, especially when a system is deployed for long periods (e.g., over decades).
Self-adaptation is designed to find reconfigurations of systems to handle such run-time inconsistencies. Planners can be used to find and enact optimal reconfigurations in such an evolving context. However, for systems that are highly configurable, such planning becomes intractable due to the size of the adaptation space.
To overcome this challenge, in this paper we explore an approach that (a) uses machine learning to find Pareto-optimal configurations without needing to explore every configuration, and (b) restricts the search space to such configurations to make planning tractable.
We explore this in the context of robot missions that need to consider task timeliness and energy consumption. An independent evaluation shows that our approach results in high quality adaptation plans in uncertain and adversarial environments.
Abstract: Why is it so difficult to build self-adaptive systems by reusing existing
self-adaptation services and frameworks? In this paper, we
argue that one possible explanation is that there is a fundamental
mismatch between the adaptation needs of modern software
systems, and the architectural models and adaptation mechanisms
supported by current self-adaptation solutions. We identify and
discuss the main reasons leading to this problem by looking into a
number of representative self-adaptation solutions that have been
proposed in recent years, including open source frameworks and
cloud-based services, from two perspectives: generality, i.e., their
ability to support a variety of architectural models and adaptation
mechanisms, and reusability, i.e., their ability to be reused without
requiring substantial effort from software developers. We then make
the case that recent industry progress toward microservices and
their enabling technologies can open the way to the development
of more general and reusable self-adaptation solutions.
Abstract: Design and verification of modern systems requires diverse models,
which often come from a variety of disciplines, and it is challenging to manage
their heterogeneity – especially in the case of cyber-physical systems. To check
consistency between models, recent approaches map these models to flexible static
abstractions, such as architectural views. This model integration approach, however, comes at a cost of reduced expressiveness because complex behaviors of the
models are abstracted away. As a result, it may be impossible to automatically
verify important behavioral properties across multiple models, leaving systems
vulnerable to subtle bugs. This paper introduces the Integration Property Language (IPL) that improves integration expressiveness using modular verification of
properties that depend on detailed behavioral semantics while retaining the ability
for static system-wide reasoning. We prove that the verification algorithm is sound
and analyze its termination conditions. Furthermore, we perform a case study on a
mobile robot to demonstrate IPL is practically useful and evaluate its performance.
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Abstract: Research in self-adaptive systems often uses web applications as
target systems, running the actual software on real web servers.
This approach has three drawbacks. First, these systems are not
easy and/or cheap to deploy. Second, run-time conditions cannot
be replicated exactly to compare diferent adaptation approaches
due to uncontrolled factors. Third, running experiments is time-
consuming. To address these issues, we present SWIM, an exemplar
that simulates a web application. SWIM can be used as a target
system with an external adaptation manager interacting with it
through its TCP-based interface. Since the servers are simulated,
this use case addresses the irst two problems. The full beneit
of SWIM is attained when the adaptation manager is built as a
simulation module. An experiment using a simulated 60-server
cluster, processing 18 hours of traic with 29 million requests takes
only 5 minutes to run on a laptop computer. SWIM has been used
for evaluating self-adaptation approaches, and for a comparative
study of model-based predictive approaches to self-adaptation.
Abstract: Due to the fundamental trade-off between quality and timeliness of planning, designers of self-adaptive systems often have to compromise between an approach that is quick to find an adaptation plan and an approach that is slow but finds a quality adaptation plan. To deal with this trade-off, in our previous work, we proposed a hybrid planning approach that combines a deliberative and a reactive planning approach to find a balance between quality and timeliness of planning.
However, when reactive and deliberative planning is combined to
instantiate a hybrid planner, the key challenge is to decide which
approach(es) should be invoked to solve a planning problem. To
this end, this paper proposes to use a data-driven instance-based
learning to find an appropriate combination of the two planning
approaches when solving a planning problem. As an initial proof
of concept, the paper presents results of a small experiment that
demonstrates the potential of the proposed approach to identify a
combination of the two planning approaches to solve a planning
problem.
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Abstract: Designing software in a way that provides guarantees about run-time behavior while achieving an acceptable balance between multiple extra-functional properties is still an open problem. Tools and techniques to inform engineers about poorly-understood design spaces are needed. To tackle this problem, we propose an approach that combines synthesis of spaces of system design alternatives from formal specifications of architectural styles with quantitative verification applied to every point of the generated solution space. The main contribution of this paper is a formal framework for specification-driven synthesis and analysis of design spaces that provides formal guarantees about the correctness of system behaviors and satisfies quantitative properties (e.g., defined over system qualities). We illustrate our proposal by analyzing the configuration space of a Tele Assistance System (TAS) using a prototype implementation of our approach.
Abstract: Self-Adaptive systems are expected to adapt to unanticipated run-time
events using imperfect information about their environment. This entails handling
the effects of uncertainties in decision-making, which are not always considered
as a first-class concern. This paper contributes a formal analysis technique that
explicitly considers uncertainty in sensing when reasoning about the best way to
adapt. We illustrate our approach on a Denial of Service (DoS) attack scenario
and present some preliminary results that show the benefits of uncertainty-aware
decision-making with respect to using an uncertainty-ignorant approach.
Abstract: Modern software-intensive systems must often guarantee
certain quality requirements under changing run-time
conditions and high levels of uncertainty. Self-adaptation has
proven to be an effective way to engineer systems that can
address such challenges, but many of these approaches are purely
reactive and adapt only after a failure has taken place. To
overcome some of the limitations of reactive approaches (e.g.,
lagging behind environment changes and favoring short-term
improvements), recent proactive self-adaptation mechanisms apply
ideas from control theory, such as model predictive control
(MPC), to improve adaptation. When selecting which MPC
approach to apply, the improvement that can be obtained with
each approach is scenario-dependent, and so guidance is needed
to better understand how to choose an approach for a given
situation. In this paper, we compare CobRA and PLA, two
approaches that are inspired by MPC. CobRA is a requirements-based
approach that applies control theory, whereas PLA is
architecture-based and applies stochastic analysis. We compare
the two approaches applied to RUBiS, a benchmark system for
web and cloud application performance, discussing the required
expertise needed to use both approaches and comparing their
run-time performance with respect to different metrics.
Abstract: Approaches to self-adaptation face a fundamental
trade-off between quality and timeliness in decision-making. Due
to this trade-off, designers of self-adaptive systems often have
to find a fixed and suboptimal compromise between these two
requirements. Recent work has proposed the hybrid planning approach
that can resolve this trade-off dynamically and potentially
in an optimal way. The promise of hybrid planning is to combine
multiple planners at run time to produce adaptation plans of
the highest quality within given time constraints. However, since
decision-making approaches are complex and diverse, the problem
of combining them is even more difficult, and no frameworks for
hybrid planning. This paper makes an important step in simplifying
the problem of hybrid planning by formalizing it and decomposing
it into four simpler subproblems. These formalizations
will serve as a foundation for creating and evaluating engineering
solutions to the hybrid planning problem.
Abstract: Large software systems have to contend with a significant number of users who interact with different components of the system in various ways. The sequences of components that are used as part of an interaction define sets of behaviors that users have with the system. These can be large in number. Among these users, it is possible that there are some who exhibit anomalous behaviors - for example, they may have found back doors into the system and are doing something malicious. These anomalous behaviors can be hard to distinguish from normal behavior because of the number of interactions a system may have, or because traces may deviate only slightly from normal behavior. In this paper we describe a model-based approach to cluster sequences of user behaviors within a system and to find suspicious, or anomalous, sequences. We exploit the underlying software architecture of a system to define these sequences. We further show that our approach is better at detecting suspicious activities than other approaches, specifically those that use unigrams and bigrams for anomaly detection. We show this on a simulation of a large scale system based on Amazon Web application style architecture.
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Ivan Ruchkin, Selva Samuel,
Bradley Schmerl, Amanda Rico and
David Garlan. Challenges in Physical Modeling for Adaptation of Cyber-Physical Systems. In Workshop on MARTCPS Models at Runtime and Networked Control for Cyber Physical Systems at IEEE World Forum on the Internet of Things, Reston, Virginia. December 12-14, 2016.
https://doi.org/10.1109/wf-iot.2016.7845513.
Winner of the IEEE World Forum on the Internet of Things Best Paper Award.
Abstract: Cyber-physical systems (CPSs) mix software,
hardware, and physical aspects with equal importance. Typically,
the use of models of such systems during run time
has concentrated only on managing and controlling the cyber
(software) aspects. However, to fully realize the goals of a
CPS, physical models too have to be treated as first-class
models. This approach gives rise to three main challenges:
(a) identifying and integrating physical and software models
with different characteristics and semantics; (b) obtaining
instances of physical models at a suitable level of abstraction
for adaptation; and (c) using and adapting physical models to
control CPSs. In this position paper, we elaborate on these three
challenges and describe our vision of making physical models
first-class entities in adaptation. We illustrate this vision in the
context of power adaptation for a service robotic system.
Bradley Schmerl, Jeffrey Gennari, Alireza Sadeghi, Hamid Bagheri, Sam Malek,
Javier Cámara and
David Garlan. Architecture Modeling and Analysis of
Security in Android Systems. In Proceedings of the 10th European Conference on Software Architecture (ECSA 2016), Copenhagen, Denmark, Springer. Lecture Notes in Computer Science, Vol. 9839. November 30-December 2, 2016.
https://doi.org/10.1007/978-3-319-48992-6_21.
Abstract: Software architecture modeling is important for analyzing system quality
attributes, particularly security. However, such analyses often assume that the
architecture is completely known in advance. In many modern domains, especially
those that use plugin-based frameworks, it is not possible to have such
a complete model because the software system continuously changes. The Android
mobile operating system is one such framework, where users can install
and uninstall apps at run time. We need ways to model and analyze such architectures
that strike a balance between supporting the dynamism of the underlying
platforms and enabling analysis, particularly throughout a system�s lifetime. In
this paper, we describe a formal architecture style that captures the modifiable
architectures of Android systems, and that supports security analysis as a system
evolves. We illustrate the use of the style with two security analyses: a predicatebased
approach defined over architectural structure that can detect some common
security vulnerabilities, and inter-app permission leakage determined by model
checking. We also show how the evolving architecture of an Android device can
be obtained by analysis of the apps on a device, and provide some performance
evaluation that indicates that the architecture can be amenable for use throughout
the system�s lifetime.
Abstract: Proactive latency-aware adaptation is an approach for self-adaptive systems that improves over reactive adaptation by considering both the current and anticipated adaptation needs of the system, and taking into account the latency of adaptation tactics so that they can be started with the necessary lead time. Making an adaptation decision with these characteristics requires solving an optimization problem to select the adaptation path that maximizes an objective function over a finite look-ahead horizon. Since this is a problem of selecting adaptation actions in the context of the probabilistic behavior of the environment, Markov decision processes (MDP) are a suitable approach. However, given all the possible interactions between the different and possibly concurrent adaptation tactics, the system, and the environment, constructing the MDP is a complex task. Probabilistic model checking can be used to deal with this problem since it takes as input a formal specification of the stochastic system, which is internally translated into an MDP, and solved. One drawback of this solution is that the MDP has to be constructed every time an adaptation decision has to be made to incorporate the latest predictions of the environment behavior. In this paper we present an approach that eliminates that run-time overhead by constructing most of the MDP offline, also using formal specification. At run time, the adaptation decision is made by solving the MDP through stochastic dynamic programming, weaving in the stochastic environment model as the solution is computed. Our experimental results show that this approach reduces the adaptation decision time by an order of magnitude compared to the probabilistic model checking approach, while producing the same results.
Abstract: The term big data arose under the explosive increase of global data as a technology that is able to store and process big and varied volumes of data, providing both enterprises and science with deep insights over its clients/experiments. Cloud computing provides a reliable, fault-tolerant, available and scalable environment to harbour big data distributed management systems. Within the context of this paper we present an overview of both technologies and cases of success when integrating big data and cloud frameworks. Although big data solves much of our current problems it still presents some gaps and issues that raise concern and need improvement. Security, privacy, scalability, data governance policies, data heterogeneity, disaster recovery mechanisms, and other challenges are yet to be addressed. Other concerns are related to cloud computing and its ability to deal with exabytes of information or address exaflop computing efficiently. This paper presents an overview of both cloud and big data technologies describing the current issues with these technologies.
Abstract: In an organization, the interactions users have with software leaves patterns or traces of the parts of the systems accessed. These interactions can be associated with the underlying software architecture. The first step in detecting problems like insider threat is to detect those traces that are anomalous.
In this paper, we present a method to find anomalous users leveraging these interaction traces, categorized by user roles. We propose a model based approach to cluster user sequences and find outliers. Such a technique could be useful in finding potentially anomalous users, insiders, or compromised accounts.We show that the approach works on a simulation of a large scale system based on and Amazon Web application style.
Abstract: Modern frameworks are required to be extendable as well as secure. However, these two qualities are often at odds. In this poster we describe an approach that uses a combination of static analysis and run-time management, based on software architecture models, that can improve security while maintaining framework extendability. We implement a prototype of the approach for the Android platform. Static analysis identifi es the architecture and communication patterns among the collection of apps on an Android device and which communications might be vulnerable to attack. Run-time mechanisms monitor these potentially vulnerable communication patterns, and adapt the system to either deny them, request explicit approval from the user, or allow them.
Ivan Ruchkin,
Bradley Schmerl and
David Garlan. Analytic Dependency Loops in Architectural Models of Cyber-Physical Systems. In Proceedings of the 8th International Workshop on Model-based Architecting of Cyber-Physical and Embedded Systems, Ottawa, Canada. September 28, 2015.
Abstract: Rigorous engineering of safety-critical Cyber-
Physical Systems (CPS) requires integration of heterogeneous
modeling methods from different disciplines. It is often necessary
to view this integration from the perspective of analyses –
algorithms that read and change models. Although analytic
integration supports formal contract-based verification of model
evolution, it suffers from the limitation of analytic dependency
loops. Dependency loops between analyses cannot be resolved
based on the existing contract-based verification. This paper
makes a step towards using rich architectural description to
resolve circular analytic dependencies. We characterize the dependency
loop problem and discuss three algorithmic approaches
to resolving such loops: analysis iteration, constraint solving, and
genetic search. These approaches take advantage of information
in multi-view architectures to resolve analytic dependency loops.
Abstract: Modern software systems are often compositions
of entities that increasingly use self-adaptive capabilities to
improve their behavior to achieve systematic quality goals. Self-adaptive
managers for each component system attempt to provide
locally optimal results, but if they cooperated and potentially
coordinated their efforts it might be possible to obtain more
globally optimal results. The emergent properties that result
from such composition and cooperation of self-adaptive systems
are not well understood, difficult to reason about, and
present a key challenge in the evolution of modern software
systems. For example, the effects of coordination patterns and
protocols on emergent properties such as the resiliency of the
collectives need to be understood when designing these systems.
In this paper we propose that probabilistic model checking
of stochastic multiplayer games (SMG) provides a promising
approach to analyze, understand, and reason about emergent
properties in collectives of adaptive systems (CAS). Probabilistic
Model Checking of SMGs is a technique particularly suited
to analyzing emergent properties in CAS, since SMG models
capture: (i) the uncertainty and variability intrinsic to the CAS
and its execution environment in the form of probabilistic and
nondeterministic choices, and (ii) the competitive/cooperative
aspects of the interplay among the constituent systems of the
CAS. Analysis of SMGs allows us to reason about things like
the worst case scenarios, which constitutes a new contribution
to understanding emergent properties in CAS. We investigate
the use of SMGs to show how they can be useful in analyzing
the impact of communication topology for collections of fully
cooperative systems defending against an external attack.
Abstract: Self-adaptive systems tend to be reactive and myopic, adapting
in response to changes without anticipating what the
subsequent adaptation needs will be. Adapting reactively
can result in ineffi?ciencies due to the system performing a
suboptimal sequence of adaptations. Furthermore, when
adaptations have latency, and take some time to produce
their e?ffect, they have to be started with su?fficient lead time
so that they complete by the time their eff?ect is needed.
Proactive latency-aware adaptation addresses these issues
by making adaptation decisions with a look-ahead horizon
and taking adaptation latency into account. In this paper we
present an approach for proactive latency-aware adaptation
under uncertainty that uses probabilistic model checking for
adaptation decisions. The key idea is to use a formal model
of the adaptive system in which the adaptation decision is
left underspeci?fied through nondeterminism, and have the
model checker resolve the nondeterministic choices so that
the accumulated utility over the horizon is maximized. The
adaptation decision is optimal over the horizon, and takes
into account the inherent uncertainty of the environment
predictions needed for looking ahead. Our results show that
the decision based on a look-ahead horizon, and the factoring
of both tactic latency and environment uncertainty, considerably
improve the eff?ectiveness of adaptation decisions.
Abstract: Modern cyber-physical systems interact closely with continuous physical processes like kinematic movement. Software component frameworks do not provide an explicit way to represent or reason about these processes. Meanwhile, hybrid program models have been successful in proving critical properties of discrete-continuous systems. These programs deal with diverse aspects of a cyber-physical system such as controller decisions, component communication protocols, and mechanical dynamics, requiring several programs to address the variation. However, currently these aspects are often intertwined in mostly monolithic hybrid programs, which are difficult to understand, change, and organize. These issues can be addressed by component-based engineering, making hybrid modeling more practical. This paper lays the foundation for using architectural models to provide component-based benefits to developing hybrid programs. We build formal architectural abstractions of hybrid programs and formulas, enabling analysis of hybrid programs at the component level, reusing parts of hybrid programs, and automatic transformation from views into hybrid programs and formulas. Our approach is evaluated in the context of a robotic collision avoidance case study.
Abstract: Architecture-based approaches to self-adaptation rely on architectural descriptions to reason about the best way of adapting the structure and behavior of software-intensive systems at run time, either by choosing among a set of predefined adaptation strategies, or by automatically generating adaptation plans. Predefined strategy selection has allow computational overhead and facilitates dealing with uncertainty (e.g., by accounting explicitly for contingencies derived from unexpected outcomes of actions), but requires additional designer effort regarding the specification of strategies and is unable to guarantee optimal solutions. In contrast, run time plan generation is able to explore a richer
solution space and provide optimal solutions in some cases,
but is more limited when dealing with uncertainty, and incurs higher computational overheads. In this paper, we
propose an approach to optimal adaptation plan generation for architecture-based self-adaptation via model checking of stochastic multiplayer games (SMGs). Our approach
enables: (i) trade-off analysis among different qualities by means of utility functions and preferences, and (ii) explicit modeling of uncertainty in the outcome of adaptation actions and the behavior of the environment. Basing on the concepts embodied in the Rainbow framework for self-adaptation, we illustrate our approach in Znn.com, a case study
that reproduces the infrastructure for a news website.
Abstract: Self-adaptive systems have the ability to adapt their behavior to dynamic operation conditions. In reaction to changes in the environment, these systems determine the appropriate corrective actions based in part on information about which action will have the best impact on the system. Existing models used to describe the impact of adaptations are either unable to capture the underlying uncertainty and variability of such dynamic environments, or are not compositional and described at a level of abstraction too low to scale in terms of specification effort required for non-trivial systems. In this paper, we address these shortcomings by describing an approach to the specification of impact models based on architectural system descriptions, which at the same time allows us to represent both variability and uncertainty in the outcome of adaptations, hence improving the selection of the best corrective action. The core of our approach is an impact model language equipped with a formal semantics defined in terms of Discrete Time Markov Chains. To validate our approach, we show how employing our language can improve the accuracy of predictions used for decision-making in the Rainbow framework for architecture-based self-adaptation.
Abstract: Availability is an increasingly important quality for today's
software-based systems and it has been successfully addressed
by the use of closed-loop control systems in self-adaptive systems.
Probes are inserted into a running system to obtain
information and the information is fed to a controller that,
through provided interfaces, acts on the system to alter its
behavior. When a failure is detected, pinpointing the source
of the failure is a critical step for a repair action. However,
information obtained from a running system is commonly
incomplete due to probing costs or unavailability of probes.
In this paper we address the problem of fault localization
in the presence of incomplete system monitoring. We may
not be able to directly observe a component but we may be
able to infer its health state. We provide formal criteria to
determine when health states of unobservable components
can be inferred and establish formal theoretical bounds for
accuracy when using any spectrum-based fault localization
algorithm.
Bradley Schmerl,
Javier Cámara, Jeffrey Gennari,
David Garlan,
Paulo Casanova,
Gabriel A. Moreno,
Thomas J. Glazier and
Jeffrey M. Barnes. Architecture-Based Self-Protection: Composing and Reasoning about Denial-of-Service Mitigations. In HotSoS 2014: 2014 Symposium and Bootcamp on the Science of Security, Raleigh, NC, USA. April 8-9, 2014.
https://doi.org/10.1145/2600176.2600181.
Abstract: Security features are often hardwired into software applications, making it difficult to adapt security responses to reflect changes in runtime context and new attacks. In prior work, we proposed the idea of architecture-based self-protection as a way of separating adaptation logic from application logic and providing a global perspective for reasoning about security adaptations in the context of other business goals. In this paper, we present an approach, based on this idea, for combating denial-of-service (DoS) attacks. Our approach allows DoS-related tactics to be composed into more sophisticated mitigation strategies that encapsulate possible responses to a security problem. Then, utility-based reasoning can be used to consider diff erent business contexts and qualities. We describe how this approach forms the underpinnings of a scientific approach to self-protection, allowing us to reason about how to make the best choice of mitigation at runtime. Moreover, we also show how formal analysis can be used to determine whether the mitigations cover the range of conditions the system is likely to encounter, and the effect of mitigations on other quality attributes of the system. We evaluate the approach using the Rainbow self-adaptive framework and show how Rainbow chooses DoS mitigation tactics that are sensitive to diff erent business contexts.
Vishal Dwivedi,
David Garlan, Jürgen Pfeffer and
Bradley Schmerl. Model-based Assistance for Making Time/Fidelity Trade-offs in Component Compositions. In 11th International Conference on Information Technology : New Generations (ITNG 2014), Special track on: MDCBSE: Model-Driven, Component-Based Software Engineering, Las Vegas, NV. April 7-9, 2014.
https://doi.org/10.1109/itng.2014.107.
Abstract: In many scientific fields, simulations and analyses require compositions of computational entities such as web services, programs, and applications. In such fields, users may want various trade-offs between different qualities. Examples include: (i) performing a quick approximation vs. an accurate, but slower, experiment, (ii) using local slower execution environments vs. remote, but advanced, computing facilities, (iii) using quicker approximation algorithms vs. computationally expensive algorithms with smaller data. However, such trade-offs are difficult to make as many such decisions today are either (a) wired into a fixed configuration and cannot be changed, or (b) require detailed systems knowledge and experimentation to determine what configuration to use. In this paper we propose an approach that uses architectural models coupled with automated design space generation for making fidelity and timeliness trade-offs. We illustrate this approach through an example in the intelligence analysis domain.
Eric Yuan, Sam Malek,
Bradley Schmerl,
David Garlan and Jeffrey Gennari. Architecture-Based Self-Protecting Software Systems. In Proceedings of the Ninth International ACM Sigsoft Conference on the Quality of Software Architectures (QoSA 2013). pp. 33-42. June 17-21, 2013. DOI:
10.1145/2465478.2465479.
Abstract: Since conventional software security approaches are often manually developed and statically deployed, they are no longer sufficient against today's sophisticated and evolving cyber security threats. This has motivated the development of self-protecting software that is capable of detecting security threats and mitigating them through runtime adaptation techniques. In this paper, we argue for an architecture-based self- protection (ABSP) approach to address this challenge. In ABSP, detection and mitigation of security threats are informed by an architectural representation of the running system, maintained at runtime. With this approach, it is possible to reason about the impact of a potential security breach on the system, assess the overall security posture of the system, and achieve defense in depth. To illustrate the effectiveness of this approach, we present several architecture adaptation patterns that provide reusable detection and mitigation strategies against well-known web application security threats. Finally, we describe our ongoing work in realizing these patterns on top of Rainbow, an existing architecture-based adaptation framework.
Abstract: Many domains such as scientific computing and neuroscience require end users to compose heterogeneous computational entities to automate their professional tasks. However, an issue that frequently hampers such composition is data-mismatches between computational entities. Although, many composition frameworks today provide support for data mismatch resolution through specialpurpose data converters, end users still have to put significant effort in dealing with data mismatches, e. g., identifying the available converters and determining which of them meet their QoS expectations. In this paper we present an approach that eliminates this effort by automating the detection and resolution of data mismatches. Specifically, it uses architectural abstractions to automatically detect different types of data mismatches, model-generation techniques to fix those mismatches, and utility theory to decide the best fix based on QoS constraints. We illustrate our approach in the neuroscience domain where data-mismatches can be fixed in an efficient manner on the order of few seconds.
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Abstract: Self-diagnosis is a fundamental capability of self-adaptive
systems. In order to recover from faults, systems need
to know which part is responsible for the incorrect behavior. In
previous work we showed how to apply a design-time diagnosis
technique at run time to identify faults at the architectural level
of a system. Our contributions address three major shortcomings
of our previous work: 1) we present an expressive, hierarchical
language to describe system behavior that can be used to
diagnose when a system is behaving different to expectation; the
hierarchical language facilitates mapping low level system events
to architecture level events; 2) we provide an automatic way to
determine how much data to collect before an accurate diagnosis
can be produced; and 3) we develop a technique that allows the
detection of correlated faults between components. Our results
are validated experimentally by injecting several failures into a
system and accurately diagnosing them using our algorithm.
Abstract: Although architecture-based self-adaptation has
been widely used, there is still little understanding about
the validity and tradeoffs of incorporating it into real-world software-intensive systems which already feature built-in adaptation mechanisms. In this paper, we report on our experience in integrating Rainbow, a platform for architecture-based self-adaptation, and an industrial middleware employed to monitor and manage highly populated networks of devices. Concretely, we reflect on aspects such as the effort required for framework customization and legacy code refactoring, performance improvement, and the impact of architecture-based self-adaptation on system evolution.
David Garlan,
Vishal Dwivedi,
Ivan Ruchkin and
Bradley Schmerl. Foundations and Tools for End-User Architecting. In Large-Scale Complex IT Systems. Development, Operation and Management, 17th Monterey Workshop 2012, Oxford, UK, March 19-21, 2012,
David Garlan and Radu Calinescu (Eds.), Springer. Lecture Notes in Computer Science, Vol. 7539, pp. 157-182. 2012.
https://doi.org/10.1007/978-3-642-34059-8_9.
Abstract: Within an increasing number of domains an important emerging need is the ability for technically naive users to compose computational elements into novel configurations. Examples include astronomers who create new analysis pipelines to process telescopic data, intelligence analysts who must process diverse sources of unstructured text to discover socio-technical trends, and medical researchers who have to process brain image data in new ways to understand disease pathways. Creating such compositions today typically requires low-level technical expertise, limiting the use of computational methods and increasing the cost of using them. In this paper we describe an approach -- which we term end-user architecting -- that exploits the similarity between such compositional activities and those of software architects. Drawing on the rich heritage of software architecture languages, methods, and tools, we show how those techniques can be adapted to support end users in composing rich computational systems through domain-specific compositional paradigms and component repositories, without requiring that they have knowledge of the low-level implementation details of the components or the compositional infrastructure. Further, we outline a set of open research challenges that the area of end-user architecting raises.
Abstract: Computations are pervasive across many domains, where end users have to compose various heterogeneous computational entities to perform professional activities. Service-Oriented Architecture (SOA) is a widely used mechanism that can support such forms of compositions as it allows heterogeneous systems to be wrapped as services that can then be combined with each other. However, current SOA orchestration languages require writing scripts that are typically too low-level for end users to write, being targeted at professional programmers and business analysts. To address this problem, this paper proposes an approach based on an architectural-style for service orchestration using an end user specification style called SCORE. SCORE uses high-level constructs that can be tailored for different domains and automatically translated into executable constructs by tool support. We demonstrate the usefulness of SCORE across two domains – dynamic network analysis and neuroscience, where users are intelligence analysts and scientists respectively. They can use an architectural style based vocabulary as a basis of their domain-specific
Abstract: An important step in achieving robustness to run-time faults is the ability
to detect and repair problems when they arise in a running system. Effective
fault detection and repair could be greatly enhanced by run-time fault diagnosis
and localization, since it would allow the repair mechanisms to focus adaptation
effort on the parts most in need of attention. In this paper we describe
an approach to run-time fault diagnosis that combines architectural models with
spectrum-based reasoning for multiple fault localization. Spectrum-based reasoning
is a lightweight technique that takes a form of trace abstraction and produces
a list (ordered by probability) of likely fault candidates. We show how this technique
can be combined with architectural models to support run-time diagnosis
that can (a) scale to modern distributed software systems; (b) accommodate the
use of black-box components and proprietary infrastructure for which one has
neither a specification nor source code; and (c) handle inherent uncertainty about
the probable cause of a problem even in the face of transient faults and faults that
arise only when certain combinations of system components interact.
Abstract: Current methods for modeling, analysis, and design
of cyber-physical systems lack a unifying framework due to the
complexity and heterogeneity of the constituent elements and
their interactions. Our approach is to define relationships between
system models at the architectural level, which captures the
structural interdependencies and some semantic interdependencies
between representations without attempting to comprehend
all of the details of any particular modeling formalism. This
paper addresses the issue of defining and evaluating consistency
between architectural views imposed by various heterogeneous
models and a base architecture (BA) for the complete system. This
notion of structural consistency ensures that the model elements
adhere to the cyber and physical types and the connections
between components present in the BA, which serves as the
unifying framework for model-based development. Consistency
checking between a model and the underlying system architecture
is formulated as a typed graph matching problem between
the connectivity graphs of the corresponding architectural view
and the system’s BA. The usefulness of the approach to check
system modeling assumptions is illustrated in the context of two
heterogeneous views of a quadrotor air vehicle.
Abstract: An increasingly important class of software-based systems is plat-forms that permit integration of third-party components, services, and tools. Service-Oriented Architecture (SOA) is one such plat-form that has been successful in providing integration and distri-bution in the business domain, and could be effective in other domains (e.g., scientific computing, healthcare, and complex deci-sion making). In this paper, we discuss our application of SOA to provide an integration platform for socio-cultural analysis, a domain that, through models, tries to understand, analyze and predict relationships in large complex social systems. In developing this platform, called SORASCS, we had to overcome issues we believe are generally applicable to any application of SOA within a domain that involves technically naïve users and seeks to establish a sustainable software ecosystem based on a common integration platform. We discuss those issues and the lessons learned about the kinds of problems that occur, and pathways toward a solution.
Ajinkya Y. Bhave, Bruce Krogh,
David Garlan and
Bradley Schmerl. Multi-domain Modeling of Cyber-Physical Systems using Architectural Views. In Proceedings of the 1st Analytic Virtual Integration of Cyber-Physical Systems Workshop. November 30, 2010. Co-located with RTSS 2010.
Abstract: Designing cyber-physical systems(CPSs)increasingly requires the use of multi-domain models throughout the development process. Ensuring consistent relationships between various system models is an important part of an integrated design methodology. This paper describes an architectural approach to reasoning about relations between heterogeneous system models. The runtime base architecture of the system is used as a unifying representation to compare the structure and semantics of the associated models. Each model is related to the base architecture through the abstraction of an architectural view, which captures structural and semantic correspondences between model elements and system entities. The use of the architectural view framework to relate system models from different domains is illustrated in the context of a quadrotor air vehicle.
Ajinkya Y. Bhave,
David Garlan, Bruce Krogh, Akshay Rajhans and
Bradley Schmerl. Augmenting Software Architectures with Physical Components. In Proceedings of the Embedded Real Time Software and Systems Conference (ERTS^2 2010). May 19-21, 2010.
Abstract: This paper presents an extension of existing software architecture tools to model physical systems, their interconnections, and the interactions between physical and cyber components. We introduce a new cyber-physical system (CPS) architectural style to support the construction of architectural descriptions of complete systems and to serve as the reference context for analysis and evaluation of design alternatives using existing model-based tools. The implementation of the CPS architectural style in AcmeStudio includes behavioral annotations on components and connectors using either finite state processes (FSP) or linear hybrid automata (LHA) with plug-ins to perform behavior analysis. The application of the CPS architectural style is illustrated for the STARMAC quadrotor.
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Andrew Faulring, Brad Myers, Ken Mohnkern,
Bradley Schmerl, Aaron Steinfeld, John Zimmerman, Asim Smailagic, Jeffery Hansen and Daniel Siewioreck. Agent-Assisted Task Management that Reduces Email Overhead. In Proceedings of the 14th International Conference on Intelligent User Interfaces, Hong Kong, China, ACM Press. pp. 61-70. February 7-10, 2010. Honorable Mention Award in the Best Paper
Contest.
DOI.
Abstract: RADAR is a multiagent system with a mixed-initiative user
interface designed to help office workers cope with email
overload. RADAR agents observe experts to learn models
of their strategies and then use the models to assist other
people who are working on similar tasks. The agents’ assistance helps a person to transition from the normal email-centric workflow to a more efficient task-centric workflow.
The Email Classifier learns to identify tasks contained within emails and then inspects new emails for similar tasks. A
novel task-management user interface displays the found
tasks in a to-do list, which has integrated support for performing the tasks. The Multitask Coordination Assistant
learns a model of the order in which experts perform tasks
and then suggests a schedule to other people who are working on similar tasks. A novel Progress Bar displays the suggested schedule of incomplete tasks as well as the completed tasks. A large evaluation demonstrated that novice
users confronted with an email overload test performed
significantly better (a 37% better overall score with a factor
of four fewer errors) when assisted by the RADAR agents.
Abstract: As new market opportunities, technologies, platforms, and frameworks become available, systems require large-scale and systematic architectural restructuring to accommodate them. Today’s architects have few tools and techniques to help them plan this architecture evolution. In particular, they have little assistance in planning alternative evolution paths, trading-off various aspects of the different paths, or knowing best practices for particular domains. In this paper we describe an approach for assisting architects in developing and reasoning about architectural evolution paths. The key in-sight of our approach is that, architecturally, many system evolutions follow certain common patterns – or evolution styles. We define what we mean by an evolution style, and show how it can be used to provide automated assistance for expressing architectural evolution, and for reasoning about both the correctness and quality of evolution paths.
Abstract: One common approach to self-adaptive systems is to incorporate a control layer that monitors a system, supervisorily detects problems, and applies adaptation strategies to fix problems or improve system behavior. While such approaches have been found to be quite effective, they are typically limited to carrying out a single adaptation at a time, delaying other adaptations until the current one finishes. This in turn leads to a problem in which a time-critical adaptation may have to wait for an existing long-running adaptation to complete, thereby missing a window of opportunity for that adaptation. In this paper we improve on existing practice through an approach in which adaptations can be preempted to allow for other time-critical adaptations to be scheduled. Scheduling is based on an algorithm that maximizes time-related utility for a set of concurrently executing adaptations.
David Garlan, Kathleen M. Carley,
Bradley Schmerl, Michael W. Bigrigg and
Orieta Celiku. Using Service-Oriented Architectures for Socio-Cultural Analysis. In Proceedings of the 21st International Conference on Software Engineering and Knowledge Engineering (SEKE2009), Boston, USA. July 1-3, 2009.
Abstract: An emergent domain that raises some unique engineering challenges is that of software architectures to convert large bodies of unstructured data to human-usable knowledge, such as in the domain of socio-cultural information analysis. We report on an architecture based on Service-Oriented Architectures that we are applying in this domain. We list the requirements that such an architecture must support, describe our architecture for addressing them, and outline what we believe are the important engineering and research issues that must still be overcome.
Abstract: Architecture evolution is a key feature of most software systems. There are few tools that help architects plan and execute these evolutionary paths. We demonstrate a tool to enable architects to describe evolution paths, associate properties with elements of the paths, and perform trade-off analysis over these paths.
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Abstract: Rainbow is a framework for engineering a system with run-time, self-adaptive capabilities to monitor, detect, decide, and act on opportunities for system improvement. We applied Rainbow to a system, Znn.com, and evaluated its effectiveness to self-adapt on three levels: its effectiveness to maintain quality attribute in the face of changing conditions, run-time overheads of adaptation, and the engineering effort to use it to add self-adaptive capabilities to Znn.com. We make Znn.com and the associated evaluation tools available to the community so that other researchers can use it to evaluate their own systems and the community can compare different systems. In this paper, we report on our evaluation experience, reflect on some principles for benchmarking self-adaptive systems, and discuss the suitability of our evaluation tools for this purpose.
Abstract: Rainbow is an approach for engineering self-adaptive systems, with run-time, closed-loop control over target systems to monitor, detect, decide, and act on opportunities for system improvement. RAIDE enables adaptation engineers to customize the Rainbow framework, simulate adaptation behavior, and deploy Rainbow run-time components.
Abstract: Most pervasive computing technologies focus on helping users with computer-oriented tasks. In this NSF-funded project, we instead focus on using computers to support user-centered “activities” that normally do not involve the use of computers. Examples may include everyday tasks around such as answering the doorbell or doing laundry. A focus on activity-based computing brings to the foreground a number of unique challenges. These include activity definition and representation, system design, interfaces for managing activities, and ensuring robust operation. Our project focuses on the first two challenges.
Abstract: This paper introduces an architectural style for enabling end-users to quickly design and deploy software systems in domains characterized by highly personalized and dynamic requirements.
The style offers an intuitive metaphor based on boxes, pipes, and wires, but retains enough preciseness that systems can be automatically assembled and dynamically reconfigured based on uDesign descriptions. uDesign was primarily motivated and validated within monitoring and control applications for smart spaces, but we envision possible extensions to other domains.
Our contribution differs from early attempts at end-user programming in the level of abstraction, software architecture rather than programming, and in the subject of description: run-time rather than code structures.
To validate the approach, the paper presents (a) two case studies, one in health care and one in home security, (b) the formal semantics of uDesign’s primitives, and (c) a mapping of those primitives to an existing software infrastructure: the Aura infrastructure.
Abstract: Notations and techniques for architectural modeling and analysis have matured considerably over the past two decades. However, to date these approaches have primarily focused on architectural properties and behavior that can be precisely defined. In this paper we argue that it is possible to augment existing architecture description languages (ADLs) to support reasoning and analysis in the presence of uncertainty. Specifically, we outline two basic extensions to formal architecture descriptions that take advantage of probabilistic specifications to support architecture-based analyses such as simulation, detection of behavioral drift, and reasoning about the expected outcomes of uncertain behavior. An important property of these specifications is that they allow incremental refinement � as more is known about the behavior of the system, specifications can be extended without invalidating previous analyses.
Abstract: Self-adapting systems based on multiple concurrent applications must decide how to allocate scarce resources to
applications and how to set the quality parameters of each
application to best satisfy the user. Past work has made
those decisions with analytic models that used current resource availability information: they react to recent changes in resource availability as they occur, rather than anticipating future availability. These reactive techniques may model each local decision optimally, but the accumulation of decisions over time nearly always becomes less than optimal.
In this paper, we propose an approach to self adaptation, called anticipatory configuration that leverages
predictions of future resource availability to improve utility for the user over the duration of the task. The approach solves the following technical challenges: (1) how to express resource availability prediction, (2) how to combine prediction from multiple sources, and (3) how to leverage predictions continuously while improving utility to the user. Our experiments show that when certain adaptation operations are costly, anticipatory configuration provides better utility to the user than reactive configuration, while being comparable in resource demand.
David Garlan and
Bradley Schmerl. An Architecture for Personal Cognitive Assistance. In 18th International Conference on Software Engineering and Knowledge Engineering, San Francisco Bay, USA. July 5-7, 2006.
Abstract: Current desktop environments provide weak support for carrying out complex user-oriented tasks. Although individual applications are becoming increasingly sophisticated and featurerich, users must map their high-level goals to the low-level operational vocabulary of applications, and deal with a myriad of routine tasks (such as keeping up with email, keeping calendars and web sites up-to-date, etc.). An alternative vision is that of a personal cognitive assistant. Like a good secretary, such an assistant would help users accomplish their high-level goals, coordinating the use of multiple applications, automatically handling routine tasks, and, most importantly, adapting to the individual needs of a user over time. In this paper we describe the architecture and its implementation for a personal cognitive assistant called RADAR. Key features include (a) extensibility through the use of a plug-in agent architecture (b) transparent integration with legacy applications and data of today?s desktop environments, and (c) extensive use of learning so that the environment adapts to the individual user over time.
Abstract: In the world of autonomic computing, the ultimate aim is to automate human tasks in system management to achieve high-level stakeholder objectives. One common approach is to capture and represent human expertise in a form executable by a computer. Techniques to capture such expertise in programs, scripts, or rule sets are effective to an extent. However, they are often incapable of expressing the necessary adaptation expertise and emulating the subtleties of trade-offs in high-level decision making. In this paper, we propose a new language of adaptation that is suffi-ciently expressive to capture the subtleties of choice, deriving its ontology from system administration tasks and its underlying formalism from utility theory.
David Garlan and
Bradley Schmerl. Architecture-driven Modelling and Analysis. In Proceedings of the 11th Australian Workshop on Safety Related Programmable Systems (SCS'06), Tony Cant (Ed.), Melbourne, Australia. Conferences in Research and Practice in Information Technology, Vol. 69. 2006.
Abstract: Over the past 15 years there has been increasing recognition that careful attention to the design of a system�s software architecture is critical to satisfying its requirements for quality attributes such as performance, security, and dependability. As a consequence, during this period the field of software architecture has matured significantly. However, current practices of software architecture rely on relatively informal methods, limiting the potential for fully exploiting architectural designs to gain insight and improve the quality of the resulting system. In this paper we draw from a variety of research results to illustrate how formal approaches to software architecture can lead to enhancements in software quality, including improved clarity of design, support for analysis, and assurance that implementations conform to their intended architecture.
Abstract: One of the challenges for software architects is ensuring that an implemented system faithfully represents its architecture. We describe and demonstrate a tool, called DiscoTect, that addresses this challenge by dynamically monitoring a running system and deriving the software architecture as that system runs. The derivation process is based on mappings that relate low level system-level events to higher-level architectural events. The resulting architecture is then fed into existing architectural design tools so that comparisons can be conducted with the design time architecture and architectural analyses can be re-run to ensure that they are still valid. In addition to the demonstration, we briefly describe the mapping language and formal definition of the language in terms of Colored Petri Nets.
Abstract: Developing dependable software systems requires enforcing conformance between architecture and implementation during software development and evolution. We address this problem with a multi-pronged approach: (a) automated refinement of a component-and-connector (C&C) architectural view into an initial implementation, (b) enforcement of architectural structure at the programming language level, (c) automated abstraction of a C&C view from an implementation, and (d) semi-automated incremental synchronization between the architectural and the implementation C&C views.
We use an Architecture Description Language (ADL), Acme, to describe the architecture, and ArchJava, an implementation language which embeds a C&C architecture specification within Java implementation code. Although both Acme and ArchJava specify C&C views, a number of structural differences may arise. Our approach can detect structural differences which correspond directly to implementation-level violations of the well thoughtout architectural intent. Furthermore, supplementing the C&C view extracted from the implementation with architectural types and styles can uncover additional violations.
Marwan Abi-Antoun, Jonathan Aldrich,
David Garlan,
Bradley Schmerl, Nagi Nahas and Tony Tseng. Software Architecture with Acme and ArchJava (Research Demonstration). In Proceedings of the 27th International Conference on Software Engineering, St. Louis, MS. May, 2005.
Abstract: We demonstrate a tool to incrementally synchronize an Acme architectural model described in the Acme Architectural Description Language (ADL) with an implementation in ArchJava, an extension of the Java programming language that includes explicit architectural modeling constructs.
David Garlan, William K. Reinholtz,
Bradley Schmerl,
Nicholas Sherman and Tony Tseng. Bridging the Gap between Systems Design and Space Systems Software. In Proceedings of the 29th Annual IEEE/NASA Software Engineering Workshop (SEW-29), Greenbelt, MD. April 6-7, 2005.
Abstract: A challenging problem for software engineering practitioners is moving from high-level system architectures produced by system engineers to deployable software produced by software engineers. In this paper we describe our experience working with NASA engineers to develop an approach and toolset for automating the generation of space systems software from architectural specifications. Our experience shows that it is possible to leverage the space systems domain, formal architectural specifications, and component technology to provide retargetable code generators for this class of software.
João Sousa,
Vahe Poladian,
David Garlan and
Bradley Schmerl. Capitalizing on Awareness of User Tasks for Guiding Adaptation. In Proceedings of the First International Workshop on Adaptive and Self-managing Enterprise Applications, at CAISE'05, Portugal. 2005.
Abstract: Computers support more and more tasks in the personal and professional activities of users. Such user tasks increasingly span large periods of time and many locations across the enterprise space and beyond. Recently there has been a growing interest in developing applications that can cope with the specific environmental conditions at each location, and adapt to dynamic changes in system resources. However, in a given situation there may be many possible configuration solutions, and an awareness of the user's intent for each task is a critical element in knowing which one to pick. In this paper, we discuss the limitations of building such awareness into applications, and propose to factor the awareness of user tasks into a common software layer. That however, brings up the problem of coordinating the system-wide adaptation performed by such a layer with fine-grain adaptation performed by resource-aware applications. We summarize the main features of an architectural framework that incorporates such a layer, and distill some of the lessons learned in implementing the framework.
Shang-Wen Cheng,
David Garlan and
Bradley Schmerl. Making Self-Adaptation an Engineering Reality. In Proceedings of the Conference on Self-Star Properties in Complex Information Systems, Ozlap Babaoghu, Mark Jelasity, Alberto Montroser, Christof Fetzer, Stefano Leonardi and Aad Van Moorsel (Eds.), Springer-Verlag. LNCS, Vol. 3460. 2005. Also available from Springer-Verlag
here.
Abstract: In this paper, we envision a world where a software engineer could take an existing software system, specify, for a set of properties of interest, an objective, conditions for change, and strategies for their adaptation and, within a few man weeks, make that system self-adaptive where it was not before. We describe how our approach generalizes to different classes of systems and holds promise for cost-effective, dynamic system self-adaptation to become an engineering reality.
Abstract: Recently there has been increasing interest in developing systems that can adapt dynamically to cope with changing environmental conditions and unexpected system errors. Most efforts for achieving self-adaptation have focused on the mechanisms for detecting opportunities for improvement and then taking appropriate action. However, such mechanisms beg the question: what is the system trying to achieve? In a given situation there may be many possible adaptations, and knowing which one to pick is a difficult question. In this paper we advocate the use of explicit representation of user task as a critical element in addressing this missing link.
Abstract: A common approach to adding self-management capabilities to a system is to provide one or more external control modules, whose responsibility is to monitor system behavior, and adapt the system at run time to achieve various goals (configure the system, improve performance, recover from faults, etc.). An important problem arises when there is more than one such self-management module: how can one make sure that they are composed to provide consistent and complementary benefits? In this paper we describe a solution that introduces a self-management coordination architecture and infrastructure to support such composition. We focus on the problem of coordinating self-configuring and self-healing capabilities, particularly with respect to global configuration and incremental repair. We illustrate the approach in the context of a self-managing video teleconference system that composes two pre-existing adaptation modules to achieve synergistic benefits of both.
Abstract: Over the past decade a number of architecture description languages (ADLs) have been proposed to facilitate modeling and analysis of software architecture. While each claims to have various benefits, to date there have been few studies to assess the relative merits of these approaches. In this paper we describe our experience using two ADLs to model a system initially described in UML, and compare their effectiveness in identifying system design flaws. We also describe the techniques we used for extracting architectural models from a UML system description.
Abstract: Software architectural modeling is crucial to the development of high-quality software. Software engineering tool support is required for this activity, so that models can be developed, viewed, analyzed, and refined to implementations. This support needs to be provided in a flexible and extensible manner so that the tools can fit into a company s process and can use particular, perhaps company-defined, domain-specific architectural styles. Early architecture development environments were restricted to supporting particular architectural styles, and were not easy to tailor to other styles or domains. In this research demonstration, we describe AcmeStudio, a styleneutral architecture development environment that can (1) allow architects to tailor the environment for particular domains; (2) be used as a framework for integrating domain-specific architectural analyses; and (3) can be extended to integrate with other tools that are used in a company s software development process. We illustrate the main features of AcmeStudio and provide some insight into how we have tailored it to real-world domains specific to NASA and Ford Motor Company.
Abstract: One of the challenging problems for software developers is guaranteeing that a system as built is consistent with its architectural design. In this paper we describe a technique that uses run time observations about an executing system to construct an architectural view of the system. With this technique we develop mappings that exploit regularities in system implementation and architectural style. These mappings describe how low-level system events can be interpreted as more abstract architectural operations. We describe the current implementation of a tool that uses these mappings, and show that it can highlight inconsistencies between implementation and architecture.
Abstract: One crucial aspect of high quality software engineering is the development of a welldefined software architectural model. Despite advances in using software architectural models to clarify system design, there remains the problem of determining whether the system as implemented has the architecture as designed? One approach is to to this is to monitor the running system and translate observed events to events that construct and update an architectural model that reflects the actual running system. One can then compare this dynamically-determined model to the correct architectural model. Discrepancies can be used to flag implementation errors, or, possibly, to effect run-time adaptations to correct certain kinds of flaws. At Carnegie Mellon University, our research group has been investigating the use of system monitoring and reflection using architectural models. In the process of exploring this area we have identified a number of significant research challenges. In this paper we outline our experience, and use that as a way to lay out an agenda for architecture-based approaches to system monitoring and system self-repair.
Kevin Steppe,
David Garlan, Greg Bylenok,
Bradley Schmerl and Nataliya Shevchenko. Tool Support for Model Based Architectural Design for Automotive Control Systems. In First European Workshop on Model Driven Architecture with Emphasis on Industrial Application, Enschede, The Netherlands. March 17-19, 2004.
Abstract: In conjunction with Ford Motor Company, we built a tool to support multilevel architectural design. The tool, called Synergy, allows Ford to visually design architectures of vehicle control components. The components are imported from existing Simulink models; then the tool automatically generates a detailed view showing all required connections and ports. The resulting model is exported to Simulink for further analysis. In this paper we describe the conceptual and technical challenges encountered in building Synergy and our design choices for solving them.
Kevin Steppe, Greg Bylenok,
David Garlan,
Bradley Schmerl, Kanat Abirov and Nataliya Shevchenko. Two-tiered Architectural Design for Automotive Control Systems: An Experience Report. In Automotive Software Workshop on Future Generation Software Architectures in the Automotive Domain, San Diego, CA. January 10-12, 2004. (
Workshop Proceedings).
Abstract: An attractive approach to architecture-based design is to structure the development process into two tiers. The top tier represents the abstract design (or architecture) of a system in terms of abstract components. The bottom tier refines that design by making specific implementation decisions, such as platform, middleware, and component implementations. While attractive in principle, there has been relatively little industrial-based experience to shed light on problems and solutions involved in such an approach. In this paper we describe our experience in developing tools to introduce a two-tiered model-based approach to the design of Ford Motor Company s automotive control systems, highlighting the principle challenges, and evaluating the effectiveness of our solutions for them.
Abstract: Traditional mechanisms that allow a system to detect and recover from errors are typically wired into applications at the level of code where they are hard to change, reuse, or analyze. An alterna-tive approach is to use externalized adaptation: one or more mod-els of a system are maintained at run time and external to the ap-plication as a basis for identifying problems and resolving them. In this paper we provide an overview of recent research in which we use architectural models as the basis for such problem diagno-sis and repair. These models can be specialized to the particular style of the system, the quality of interest, and the dimensions of run time adaptation that are permitted by the running system.
Shang-Wen Cheng,
David Garlan,
Bradley Schmerl,
João Sousa,
Bridget Spitznagel and Peter Steenkiste. Using Architectural Style as a Basis for Self-repair. In Software Architecture: System Design, Development, and Maintenance (Proceedings of the 3rd Working IEEE/IFIP Conference on Software Architecture), Jan Bosch, Morven Gentleman, Christine Hofmeister and Juha Kuusela (Eds.), Kluwer Academic Publishers. pp. 45-59. August 25-31, 2002.
https://doi.org/10.1007/978-0-387-35607-5_3.
Abstract: An increasingly important requirement for software systems is the capability to adapt at run time in order to accommodate varying resources, system errors, and changing requirements. For such self-repairing systems, one of the hard problems is determining when a change is needed, and knowing what kind of adaptation is required. Recently several researchers have explored the possibility of using architectural models as a basis for run time monitoring, error detection, and repair. Each of these efforts, however, has demonstrated the feasibility of using architectural models in the context of a specific style. In this paper we show how to generalize these solutions by making architectural style a parameter in the monitoring/repair framework and its supporting infrastructure. The value of this generalization is that it allows one to tailor monitoring/ repair mechanisms to match both the properties of interest (such as performance or security), and the available operators for run time adaptation.
Abstract: In an increasing number of domains software is now required to be self-adapting and self-healing. While in the past such abilities were incorporated into software on a per system basis, proliferation of such systems calls for more generalized mechanisms to manage dynamic adaptation. General mechanisms have the advantage that they can be reused in numerous systems, analyzed separately from the system being adapted, and easily changed to incorporate new adaptations. Moreover, they provide a natural home for encoding the expertise of system designers and implementers about adaptation strategies and policies. In this paper, we show how architecture description languages and tools can be extended to provide such generalized dynamic adaptation mechanisms.
Abstract: Grid applications must increasingly self-adapt dynamically to changing environments. In most cases, adaptation has been implemented in an ad hoc fashion, on a perapplication basis. This paper describes work which generalizes adaptation so that it can be used across applications by providing an adaptation framework. This framework uses a software architectural model of the system to analyze whether the application requires adaptation, and allows repairs to be written in the context of the architectural model and propagated to the running system. In this paper, we exemplify our framework by applying it to the domain of load-balancing a client-server system. We report on an experiment conducted using our framework, which illustrates that this approach maintains architectural requirements.
Shang-Wen Cheng,
David Garlan,
Bradley Schmerl,
João Sousa,
Bridget Spitznagel, Peter Steenkiste and Ningning Hu. Software Architecture-based Adaptation for Pervasive Systems. In International Conference on Architecture of Computing Systems (ARCS'02): Trends in Network and Pervasive Computing, H Schmeck, T Ungerer and L Wolf (Eds.), Karlsruhe, Germany, Springer-Verlag. Lecture Notes in Computer Science, Vol. 2299. April 8-11, 2002.
https://doi.org/10.1007/3-540-45997-9_7.
Abstract: An important requirement for pervasive computing systems is the ability to adapt at runtime to handle varying resources, user mobility, changing user needs, and system faults. In this paper we describe an approach in which dynamic adaptation is supported by the use of software architectural models to monitor an application and guide dynamic changes to it. The use of externalized models permits one to make reconfiguration decisions based on a global per-spective of the running system, apply analytic models to determine correct re-pair strategies, and gauge the effectiveness of repair through continuous system monitoring. We illustrate the application of this idea to pervasive computing systems, focusing on the need to adapt based on performance-related criteria and models.
David Garlan,
Bradley Schmerl and
Jichuan Chang. Using Gauges for Architecture-Based Monitoring and Adaptation. In Proceedings of the Working Conference on Complex and Dynamic Systems Architecture. December 12-14, 2001.
Abstract: An increasingly important requirement for complex systems is the capability to adapt at runtime in order to accommodate variable resources, system errors, and changing requirements. An essential aspect of adaptation is the ability to observe a system's runtime behavior and interpret those observations in terms that permit a high-level understanding of the system's status. In this paper we describe mechanisms for monitoring a running system, interpreting monitored data in terms of the system's architecture, and analyzing the system architecture to ascertain if the system's runtime behavior fits within the envelope of acceptable behavior. We illustrate the use of these mechanisms for the important special case of system performance monitoring.
Abstract: Being able to find, adapt, and incorporate disparate components to form working, reliable applications is the goal of component-based software engineering. To date, there has been a lot of research, among other things, on locating components, reasoning about component compatibility, and methods for interoperability. Pervasive computing raises a number of new challenges for component-based software engineering that heretofore have been given little attention, such as mobility, adaptability, and resource awareness. In this paper we motivate and discuss the need for research in these areas, and discuss how our work in the Aura group at Carnegie Mellon University helps to address these issues.
Jack Wileden, Krishnan Sridar,
Bradley Schmerl, John Ridgeway and Alan Kaplan. Toward Pure Polylingual Persistence. In Ninth International Workshop on Persistent Object Systems: Design, Implementation and Use (POS9), Lillehammer, Norway. September, 2000.
https://doi.org/10.1007/3-540-45498-5_7.
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Abstract: A heterogeneous persistent object system provides multiple programming language interfaces. This gives rise to the polylingual inter- operability problem, namely, how can an application uniformly process data objects that have been defined, created and persistently stored us- ing distinct programming language type models. At POS-7, we reported on the PolySPIN approach (polylingual support for persistence, interoperability and naming). A prototype based on PolySPIN, which was built as an extension to the TI/Darpa Open Object-Oriented Database, supports transparent polylingual access for persistent C++ and CLOS objects. The approach, however, assumes that objects in the persistent store are monolingual structures. That is, while a C++ application using the PolySPIN approach may access and manipulate a persistent CLOS object as if it were implemented in C++, that CLOS object must be entirely implemented in CLOS. In this paper, we report on our recent efforts toward extending PolySPIN with pure polylingual persistence capabilities. Our new approach supports transparent construction and ma- nipulation of heterolingual persistent data objects. This means that a persistent object may have in its closure objects that are defined using different languages. The pure polylingual persistence mechanism trans- parently manages heterolingual persistent objects. We illustrate the pure polylingual approach by applying it to a Java-C++ realization of the 007 benchmark as implemented using the TI/Darpa Open Object-Oriented Database.
Dan Glastonbury, Chris Marlin, Michael Read and
Bradley Schmerl. Run-time views as a debugging aid in an integrated software development environment. In 1997 Software Visualisation Workshop, Adelaide, South Australia, Flinders University, South Australia. pp. 51-59. December, 1997.
Bradley Schmerl and Chris Marlin. Versioning and Consistency for Dynamically Composed Configurations. In The 7th International Workshop on Software Configuration Management, Boston, MA, Springer, Berlin. Lecture Notes in Computer Science, Vol. 1235, pp. 49-65. May, 1997.
https://doi.org/10.1007/3-540-63014-7_5.
Abstract: Dynamically composed software systems are constructed as they execute. Although these systems are of increasing importance and arise in a number of application areas, configuration management tools do not provide much support for them. This paper presents progress towards providing enhanced configuration management techniques for dynamically composed systems. The paper commences with an example of dynamically composed systems, which is used to motivate the relevance of dynamically composed systems, and to highlight various design issues and requirements for configuration management in this domain. A model, and its associated notation DCDL, is then presented to describe particular aspects of the example. This model seamlessly integrates the notions of consistency, specified in terms of complete configurations, and version families, which are relevant to the incomplete configurations of crucial importance in understanding configurations of dynamically composed systems. DCDL is then used to provide insight into the requirements for configuration management tools for dynamically composed systems.
Abstract: Dynamically composed systems are able to incorporate new components as they execute. Therefore, configurations of these systems are not fully elaborated until at least the time that they are executed, and they are perhaps never fully elaborated. Such incomplete configurations are termed partially bound configurations. Although partially bound, it is still important to be able to analyse these configurations to ascertain whether they meet certain assumptions about their composition. We are endevouring to provide such support for the construction of dynamically composed systems through the application of configuration management concepts. One way in which these concepts can be applied in this domain is to explicitly state such assumptions and hence be able to validate partially bound configurations against these assumptions; in this way, inconsistencies can be reported as soon as they arise. This paper disucsses the issues involved in providing this kind of consistency mechanism for dynamically composed systems.
Bradley Schmerl and Chris Marlin. Defining consistency in the context of partially bound configurations of dynamically composed systems. In 6th International Workshop on Software Configuration Management - Preprint of Proceedings, Berlin, Germany. March, 1996.