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 see the forest for the trees. In this paper we present ExTrA (Explaining Tradeoffs of software Architecture design spaces), an approach to analyzing architectural design spaces that addresses these limitations and provides a basis for explaining design tradeoffs. Our approach employs dimensionality reduction techniques employed in machine learning pipelines like Principal Component Analysis (PCA) and Decision Tree Learning (DTL) to enable architects to understand how design decisions contribute to the satisfaction of extra-functional properties across the design space. Our results show feasibility of the approach in two case studies and evidence that combining complementary techniques like PCA and DTL is a viable approach to facilitate comprehension of tradeoffs in poorly-understood design spaces.

Abstract: Designing software in a way that guarantees run-time behavior while achieving an acceptable balance among multiple quality attributes is an open problem. Providing guarantees about the satisfaction of the same requirements under uncertain environments is even more challenging. Tools and techniques to inform engineers about poorly-understood design spaces in the presence of uncertainty are needed, so that engineers can explore the design space, especially when tradeoffs are crucial. To tackle this problem, we describe an approach that combines synthesis of spaces of system design alternatives from formal specifications of architectural styles with probabilistic formal verification. 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) subject to uncertainty, which is treated as a first-class entity. We illustrate our approach in two case studies: a service-based adaptive system and a mobile robotics architecture. Our results show how the framework can provide useful insights into how average case probabilistic guarantees can differ from worst case guarantees, emphasizing the relevance of combining quantitative formal verification methods with structural synthesis, in contrast with techniques based on simulation and dynamic analysis that can only provide estimates about average case probabilistic properties.

Abstract: The backbone of most software architectures and component integration frameworks is one or more architectural styles that provide a domain-speci?c design vocabulary and a set of constraints on how that vocabulary is used. Today's architectural styles are increasingly complex, involving rich vocabularies and numerous constraints. Hence, designing a sound and appropriate style becomes an intellectually challenging activity. Unfortunately, although there are numerous tools to help in the analysis of architectures for individual systems, relatively less work has been done on tools to help in the design of architectural styles. In this paper we address this gap by showing how to map an architectural style, expressed formally in an architectural description language, into a relational model that can then be checked for properties, such as whether a style is consistent, whether a style satis?es some predicates over its architectural structure, and whether two styles are compatible for composition.

Abstract: The software architecture of most systems is usually described informally and diagrammatically by means of boxes and lines. In order for these descriptions to be meaningful, the diagrams are understood by interpreting the boxes and lines in specic, conventionalized ways. The informal, imprecise nature of these interpretations has a number of limitations. In this paper we consider these conventionalized interpretations as architectural styles and provide a formal framework for their uniform denition. In addition to providing a template for precisely dening new architectural styles, this framework allows for analysis within and between dierent architectural styles.

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.

Abstract: Software systems are designed and implemented with assumptions about the environment. However, once the system is deployed,the actual environment may deviate from its expected behavior,possibly undermining desired properties of the system. To enable systematic design of systems that are robust against potential environmental deviations, we propose a rigorous notion of robustness for software systems. In particular, the robustness of a system is de-fined as the largest set of deviating environmental behaviors under which the system is capable of guaranteeing a desired property. We describe a new set of design analysis problems based on our notion of robustness, and a technique for automatically computing robustness of a system given its behavior description. We demonstrate potential applications of our robustness notion on two case studies involving network protocols and safety-critical interfaces.

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.

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: 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: 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: 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: An important issue for the specification and design of software architectures is how to combine the analysis capabilities of multiple architectural definition languages (ADLs) and their supporting toolsets. In this paper, we describe our experience of integrating three ADLs: Wright, Rapide, and Aesop. We discovered that it is possible achieve interoperability in ADL tools for a non-trivial subset of the systems describable by these languages, even though the languages have different views about architectural structure and semantics. To carry out the integration we used the Acme architectural interchange language and its supporting tools.

Abstract: An increasingly important trend in the engineering of complex systems is the design of component integration standards. Such standards define rules of interaction and shared communication infrastructure that permit composition of systems out of independently-developed parts. A problem with these standards is that it is often difficult to understand exactly what they require and provide, and to analyze them in order to understand their deeper properties. In this paper we use our experience in modeling the High Level Architecture (HLA) for Distributed Simulation to show how one can capture the structured protocol inherent in an integration standard as a formal architectural model that can be analyzed to detect anomalies, race conditions, and deadlocks.

Abstract: A software architecture should expose important system properties for consideration and analysis. Performance-related properties are frequently of interest in determining the acceptability of a given software design. In this paper we show how queueing network modeling can be adapted to support performance analysis of software architectures. We also describe a tool for transforming a software architecture in a particular style into a queueing network and analyzing its performance.

Abstract: In this report we show how to adapt the notion of “attack surface” to formally evaluate security properties at the architectural level of design and to identify vulnerabilities in architectural designs. Further we explore the application of this metric in the context of architecture-based transformations to improve security by reducing the attack surface. These transformations are described in detail and validated with a simple experiment.

Abstract: Using information flow modeling to perform security analysis is a common technique used during software design. While much theoretical work has been conducted in this area, there are few tools to assist with such analysis. In many instances the security analysis must be done by hand, requiring consid-erable expertise, time, and effort. Most available tools require custom code to be written for the analysis, and are consequently not well integrated with the software process, and not easily tailored to different security requirements. In this paper we describe the use of an Architecture Description Language (ADL) to (a) represent information flow in a software system, and (b) analyze the security-related properties of a system. We use a formal predicate-based de-scription of the security properties and policies, which allows for automated analysis of the information flow to uncover common security vulnerabilities. A key advantage to using ADLs is that security properties become declarative and can be automatically checked by constraint-based tools and without the need to write custom code.

Abstract: Architectural analysis is key to producing high quality architectures. In this demonstration we present two extensions to AcmeStudio, and domain-neutral Architecture Development Environment, to add Performance and Security Simulation. Using AcmeStudio as the integration platform for these analyses allows comparisons and trade-offs between these different quality attributes.