Abstract: Machine Translation (MT) is the backbone of multiple systems and applications leveraged everyday by users. Despite research efforts and progress in the MT domain, translation remains a challenging task and MT systems struggle when translating rare words, named entities, domain-specific terminology, idiomatic expressions and culturally specific terms. To meet the translation performance expectations of users, engineers periodically update (fine-tune) MT models to guarantee translation quality. However, with ever-growing machine learning models, fine-tuning operations become more expensive, raising serious sustainability concerns. Furthermore, not all fine-tunings guarantee increased translation quality, thus wasting compute resources.

To address this issue and enhance the sustainability of MT systems, we present Flexico, an approach to engineer self-adaptive MT systems, leveraging (i) ML-based regressors to estimate the expected benefits of fine-tuning MT models; and (ii) probabilistic model checking techniques to automate the reasoning about when the benefits of fine-tuning outweigh its costs. Our empirical evaluation on two MT models and language-pairs and across 9 domains demonstrates the predictive performance of the models that estimate the expected benefits of fine-tuning and their domain-generalizability. Flexico improves the sustainability of MT systems when compared to naive baselines and decreases the number of fine-tunings while preserving high translation quality.

Abstract: Robotic systems are typically composed of various subsystems, such as localization and navigation, each encompassing numerous configurable components (e. g., selecting different planning algorithms). Once an algorithm has been selected for a component, its associated configuration options must be set to the appropriate values. Configuration options across the system stack interact non-trivially. Finding optimal configurations for highly configurable robots to achieve desired performance poses a significant challenge due to the interactions between configuration options across software and hardware that result in an exponentially large and complex configuration space. These challenges are further compounded by the need for transferability between different environments and robotic platforms. Data efficient optimization algorithms (e. g., Bayesian optimization) have been increasingly employed to automate the tuning of configurable parameters in cyber-physical systems. However, such optimization algorithms converge at later stages, often after exhausting the allocated budget (e. g., optimization steps, allotted time) and lacking transferability. This paper proposes CURE - a method that identifies causally relevant configuration options, enabling the optimization process to operate in a reduced search space, thereby enabling faster optimization of robot performance. CURE abstracts the causal relationships between various configuration options and robot performance objectives by learning a causal model in the source (a low-cost environment such as the Gazebo simulator) and applying the learned knowledge to perform optimization in the target (e. g., Turtlebot 3 physical robot). We demonstrate the effectiveness and transferability of CURE by conducting experiments that involve varying degrees of deployment changes in both physical robots and simulation

Abstract: Microservice developers increasingly use resiliency patterns such as Retry and Circuit Breaker to cope with remote services that are likely to fail. However, there is still little research on how the invocation delays typically introduced by those resiliency patterns may impact application performance under varying workloads and failure scenarios. This paper presents a novel approach and benchmark tool for experimentally evaluating the performance impact of existing resiliency patterns in a controlled setting. The main novelty of this approach resides in the ability to declaratively specify and automatically generate multiple testing scenarios involving different resiliency patterns, which one can implement using any programming language and resilience library. The paper illustrates the benefits of the proposed approach and tool by reporting on an experimental study of the performance impact of the Retry and Circuit Breaker resiliency patterns in two mainstream programming languages (C# and Java) using two popular resilience libraries (Polly and Resilience4j), under multiple service workloads and failure rates. Our results show that, under low to moderate failure rates, both resiliency patterns effectively reduce the load over the application's target service with barely any impact on the application's performance. However, as the failure rate increases, both patterns significantly degrade the application's performance, with their effect varying depending on the service's workload and the patterns' programming language and resilience library.

Abstract: Robotic systems have subsystems with a combinatorially large configuration space and hundreds or thousands of possible software and hardware configuration options interacting non-trivially. The configurable parameters are set to target specific objectives, but they can cause functional faults when incorrectly configured. Finding the root cause of such faults is challenging due to the exponentially large configuration space and the dependencies between the robot's configuration settings and performance. This paper proposes CaRE - a method for diagnosing the root cause of functional faults through the lens of causality. CaRE abstracts the causal relationships between various configuration options and the robot's performance objectives by learning a causal structure and estimating the causal effects of options on robot performance indicators. We demonstrate CaRE's efficacy by finding the root cause of the observed functional faults and validating the diagnosed root cause by conducting experiments in both physical robots (Husky and Turtlebot 3) and in simulation (Gazebo). Furthermore, we demonstrate that the causal models learned from robots in simulation (e. g., Husky in Gazebo) are transferable to physical robots across different platforms (e. g., Husky and Turtlebot 3).

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 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 planning and reasoning about architecture evolution. Our approach focuses on providing architects with the means to model prospective evolution paths and supporting analysis to select among these candidate paths. To demonstrate the usefulness of our approach, we show how it can be applied to an actual architecture evolution. In addition, we present some theoretical results about our evolution path constraint specification language.

Abstract: Self-adaptive software systems adapt to changes in the environment, in the system itself, in their requirements, or in their business objectives. Typically, these systems attempt to maintain system goals at run time and often provide assurance that they will meet their goals under dynamic and uncertain circumstances. While significant research has focused on ways to engineer selfadaptive capabilities into both new and legacy software systems, less work has been conducted on how to assure that self-adaptation maintains system goals. For traditional, especially safety-critical software systems, assurance techniques decompose assurances into sub-goals and evidence that can be provided by parts of the system. Existing approaches also exist for composing assurances, in terms of composing multiple goals and composing assurances in systems of systems. While some of these techniques may be applied to self-adaptive systems, we argue that several significant challenges remain in applying them to self-adaptive systems in this chapter. We discuss how existing assurance techniques can be applied to composing and decomposing assurances for self-adaptive systems, highlight the challenges in applying them, summarize existing research to address some of these challenges, and identify gaps and opportunities to be addressed by future research.

Abstract: The goal of this roadmap paper is to summarize the state-of-the-art and identify research challenges when developing, deploying and managing self-adaptive software systems. Instead of dealing with a wide range of topics associated with the field, we focus on four essential topics of self-adaptation: design space for self-adaptive solutions, software engineering processes for self-adaptive systems, from centralized to decentralized control, and practical run-time verification & validation for self-adaptive systems. For each topic, we present an overview, suggest future directions, and focus on selected challenges. This paper complements and extends a previous roadmap on software engineering for self-adaptive systems published in 2009 covering a different set of topics, and reflecting in part on the previous paper. This roadmap is one of the many results of the Dagstuhl Seminar 10431 on Software Engineering for Self-Adaptive Systems, which took place in October 2010.

Abstract: The feature interaction problem arises when two or more independent features interact with each other in an undesirable manner.Feature interactions remain a challenging and important problem in emerging domains of cyber-physical systems (CPS), such as intelligent vehicles, unmanned aerial vehicles (UAVs) and the Internet of Things (IoT), where the outcome of an unexpected interaction may result in a safety failure. Existing approaches to resolving feature interactions rely on priority lists or fixed strategies, but may not be effective in scenarios where none of the competing feature actions are satisfactory with respect to system requirements. This paper proposes a novel synthesis-based approach to resolution, where a conflict among features is resolved by synthesizingan action that best satisfies the specification of desirable system behaviors in the given environmental context. Unlike existing resolution methods,our approach is capable of producing a desirable system outcome even when none of the conflicting actions are satisfactory. The effectiveness of the proposed approach is demonstrated using a case study involving interactions among safety-critical features in an autonomous drone.

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.

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.

Abstract: In previous research, we have developed a theoretical framework to help software architects make better decisions when planning software evolution. Our approach is based on representation and analysis of candidate evolution paths—sequences of transitional architectures leading from the current system to a desired target architecture. One problem with this kind of approach is that it imposes a heavy burden on the software architect, who must explicitly define and model these candidate paths. In this paper, we show how automated planning techniques can be used to support automatic generation of evolution paths, relieving this burden on the architect. We illustrate our approach by applying it to a data migration scenario, showing how this architecture evolution problem can be translated into a planning problem and solved using existing automated planning tools.

Abstract: Recent research has developed a theoretical basis for providing software architects with better support for planning and carrying out major evolutions of software systems. However, these theoretical models have yet to be implemented in a complete, integrated tool. In this paper, we consider some of the challenges involved in developing such a tool as a plug-in to an existing architecture modeling framework. We present an experience report based on the development of a prototype architecture evolution tool as a plug-in to MagicDraw, a commercial UML tool. We find that there are many practical obstacles to developing tools for architecture evolution modeling as plug-ins, and we discuss some of the key factors that plug-in developers should weigh when considering frameworks.

Abstract: Virtually all software systems of significant size and longevity eventually undergo changes to their basic architectural structure. Such changes may be prompted by new feature requests, new quality attribute requirements, changing technology, or other reasons. Whatever the cause, software architecture evolution is commonplace in real-world software projects. However, research in this area has suffered from problems of validation; previous work has tended to make heavy use of toy examples and hypothetical scenarios and has not been well supported by real-world examples. To help address this problem, this paper presents a case study of an ongoing effort at the Jet Propulsion Laboratory to rearchitect the Advanced Multimission Operations System used to operate NASA�s deep-space and astrophysics missions. Based on examination of project documents and interviews with project personnel, I describe the goals and approach of this evolution effort, then demonstrate how approaches and formal methods from previous research in architecture evolution may be applied to this evolution while using languages and tools already in place at the Jet Propulsion Laboratory.

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.

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: 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: Many software systems eventually undergo changes to their basic architectural structure. Such changes may be prompted by new feature requests, new quality attribute requirements, changing technology, or other reasons. Whatever the causes, architecture evolution is commonplace in real-world software projects. Today’s software architects, however, have few techniques to help them plan such evolution. In particular, they have little assistance in planning alternatives, making trade-offs among these different alternatives, or applying best practices for particular domains. To address this, we have developed an approach for assisting architects in planning and reasoning about software architecture evolution. Our approach is based on modeling and analyzing potential evolution paths that represent different ways of evolving the system. We represent an evolution path as a sequence of transitional architectural states leading from the initial architecture to the target architecture, along with evolution operators that characterize the transitions among these states. We support analysis of evolution paths through the definition and application of constraints that express rules governing the evolution of the systemand evaluation functions that assess path quality. Finally, a set of these modeling elements may be grouped together into an evolution style that encapsulates a body of knowledge relevant to a particular domain of architecture evolution. We evaluate this approach in three ways. First, we evaluate its applicability to real-world architecture evolution projects. This is accomplished through case studies of two very different software organizations. Second, we undertake a formal evaluation of the computational complexity of verifying evolution constraints. Finally, we evaluate the implementability of the approach based on our experiences developing prototype tools for software architecture evolution.

Abstract: Many software systems eventually undergo changes to their basic architectural structure. As systems age, they often require redesign in order to accommodate new requirements, support new technologies, or respond to changing market conditions. At present, however, software architects lack tools to assist them in developing plans for carrying out such evolution.

In previous research, we have developed an approach to support architects in reasoning about evolution. Our approach is based on formally modeling and reasoning about possible evolution paths—sequences of transitional architectures leading from the current state to some desired target architecture.

To date, much of this work has been theoretical in character. We have focused our efforts on the definition and elaboration of a theoretical framework for architecture evolution, and have done relatively little work on applying this theoretical research in practical contexts.

This report presents a case study examining architecture evolution in depth in a real-world software organization: the IT division of a major global retailer. Based on content analysis of interview data and architectural documentation, the case study examines how practicing architects plan and reason about evolution, what challenges they face in doing so, and whether our modeling approach is adequate for capturing the concerns that arise in a real-world evolution. It finds that architects face a variety of pressing challenges in planning evolutions of major software systems, and use a variety of techniques to manage these challenges. It further finds that most of the major considerations that architects described in connection within a specific evolution can be successfully modeled using our approach, suggesting that the conceptual framework and specification languages that we have developed are expressive enough to be applicable to a real-world architecture evolution.

Abstract: Analysis at the level of a runtime architecture matches the way experts reason about security or privacy better than a purely code-based strategy. However, the architecture must still be correctly realized in the implementation.

We previously developed Scholia to analyze, at compile time, communication integrity between arbitrary object-oriented code, and a rich, hierarchical intended runtime architecture, using typecheckable annotations. This paper applies Scholia to security runtime architectures. Having established traceability between the target architecture and the code, we extend Scholia to enforce structural architectural constraints. At the code level, annotations enforce local, modular constraints. At the architectural level, predicates enforce global constraints. We validate the end-to-end approach in practice using a real 3,000-line Java implementation, and enforce its conformance to a security architecture designed by an expert.