In its quest for approaches to taming uncertainty in self-adaptive systems (SAS), the research community has largely focused on solutions that adapt the SAS architecture or behaviour in response to uncertainty. By comparison, solutions that reduce the uncertainty affecting SAS (other than through the blanket monitoring of their components and environment) remain underexplored. Our previous work proposed Parley , a more nuanced, adaptive approach to SAS uncertainty reduction. To that end, we introduced an SAS architecture comprising an uncertainty reduction controller that drives the adaptive acquisition of new information within the SAS adaptation loop and a tool-supported method that uses probabilistic model checking to synthesise such controllers. The controllers generated by our method deliver optimal tradeoffs between SAS uncertainty reduction benefits and new information acquisition costs with guarantees for the satisfaction of requirements. In this article, we extend Parley to Parley+ by improving the synthesis of these controllers and by expanding the formalisation of Parley+ to prove the validity of the synthesis. We illustrate the use and extend the evaluation of the effectiveness of our approach for mobile robot navigation and service-based system SAS. The evaluation results show that Parley+ can synthesise controllers that help achieve the system’s objectives significantly better than Parley in 88.1% of the cases.

To accurately make adaptation decisions, a self-adaptive system needs precise means to analyze itself at runtime. To this end, runtime verification can be used in the feedback loop to check that the managed system satisfies its requirements formalized as temporal-logic properties. These requirements, however, may change due to system evolution or uncertainty in the environment, managed system, and requirements themselves. Thus, the properties under investigation by the runtime verification have to be dynamically adapted to represent the changing requirements while preserving the knowledge about requirements satisfaction gathered thus far, all with minimal latency. To address this need, we present a runtime verification approach for self-adaptive systems with changing requirements. Our approach uses property specification patterns to automatically obtain automata with precise semantics that are the basis for runtime verification. The automata can be safely adapted during runtime verification while preserving intermediate verification results to seamlessly reflect requirement changes and enable continuous verification. We evaluate our approach on an Arduino prototype of the Body Sensor Network and the Timescales benchmark. Results show that our approach is over five times faster than the typical approach of redeploying and restarting runtime monitors to reflect requirements changes, while improving the system’s trustworthiness by avoiding interruptions of verification.

Despite the vast body of knowledge developed by the self-adaptive systems community and the wide use of self-adaptation in industry, it is unclear whether or to what extent industry leverages output of academics. Hence, it is important for the research community to answer the question: Are the solutions developed by the self-adaptive systems community mature enough for industrial adoption? Leveraging a set of empirically-grounded guidelines for industry-relevant artifacts in self-adaptation, we develop a position to answer this question from the angle of using artifacts for evaluating research results in self-adaptation, which is actively stimulated and applied by the community

Abstract Context Ensuring safety for any sophisticated system is getting more complex due to the rising number of features and functionalities. This calls for formal methods to entrust confidence in such systems. Nevertheless, using formal methods in industry is demanding because of their lack of usability and the difficulty of understanding verification results. Objective We evaluate the acceptance of formal methods by Bosch automotive engineers, particularly whether the difficulty of understanding verification results can be reduced. Method We perform two different exploratory studies. First, we conduct a user survey to explore challenges in identifying inconsistent specifications and using formal methods by Bosch automotive engineers. Second, we perform a one-group pretest-posttest experiment to collect impressions from Bosch engineers familiar with formal methods to evaluate whether understanding verification results is simplified by our counterexample explanation approach. Results The results from the user survey indicate that identifying refinement inconsistencies, understanding formal notations, and interpreting verification results are challenging. Nevertheless, engineers are still interested in using formal methods in real-world development processes because it could reduce the manual effort for verification. Additionally, they also believe formal methods could make the system safer. Furthermore, the one-group pretest-posttest experiment results indicate that engineers are more comfortable understanding the counterexample explanation than the raw model checker output. Limitations The main limitation of this study is the generalizability beyond the target group of Bosch automotive engineers.

Self-adaptation can be realized in various ways. Rule-based approaches prescribe the adaptation to be executed if the system or environment satisfies certain conditions. They result in scalable solutions but often with merely satisfying adaptation decisions. In contrast, utility-driven approaches determine optimal decisions by using an often costly optimization, which typically does not scale for large problems. We propose a rule-based and utility-driven adaptation scheme that achieves the benefits of both directions such that the adaptation decisions are optimal, whereas the computation scales by avoiding an expensive optimization. We use this adaptation scheme for architecture-based self-healing of large software systems. For this purpose, we define the utility for large dynamic architectures of such systems based on patterns that define issues the self-healing must address. Moreover, we use pattern-based adaptation rules to resolve these issues. Using a pattern-based scheme to define the utility and adaptation rules allows us to compute the impact of each rule application on the overall utility and to realize an incremental and efficient utility-driven self-healing. In addition to formally analyzing the computational effort and optimality of the proposed scheme, we thoroughly demonstrate its scalability and optimality in terms of reward in comparative experiments with a static rule-based approach as a baseline and a utility-driven approach using a constraint solver. These experiments are based on different failure profiles derived from real-world failure logs. We also investigate the impact of different failure profile characteristics on the scalability and reward to evaluate the robustness of the different approaches.

The pervasiveness and growing complexity of software systems are challenging software engineering to design systems that can adapt their behavior to withstand unpredictable, uncertain, and continuously changing execution environments. Control theoretical adaptation mechanisms have received growing interest from the software engineering community in the last few years for their mathematical grounding, allowing formal guarantees on the behavior of the controlled systems. However, most of these mechanisms are tailored to specific applications and can hardly be generalized into broadly applicable software design and development processes. This article discusses a reference control design process, from goal identification to the verification and validation of the controlled system. A taxonomy of the main control strategies is introduced, analyzing their applicability to software adaptation for both functional and nonfunctional goals. A brief extract on how to deal with uncertainty complements the discussion. Finally, the article highlights a set of open challenges, both for the software engineering and the control theory research communities.

The development of self-adaptive software requires the engineering of an adaptation engine that controls the underlying adaptable software by feedback loops. The engine often describes the adaptation by runtime models representing the adaptable software and by activities such as analysis and planning that use these models. To systematically address the interplay between runtime models and adaptation activities, runtime megamodels have been proposed. A runtime megamodel is a specific model capturing runtime models and adaptation activities. In this article, we go one step further and present an executable modeling language for ExecUtable RuntimE MegAmodels (EUREMA) that eases the development of adaptation engines by following a model-driven engineering approach. We provide a domain-specific modeling language and a runtime interpreter for adaptation engines, in particular feedback loops. Megamodels are kept alive at runtime and by interpreting them, they are directly executed to run feedback loops. Additionally, they can be dynamically adjusted to adapt feedback loops. Thus, EUREMA supports development by making feedback loops explicit at a higher level of abstraction and it enables solutions where multiple feedback loops interact or operate on top of each other and self-adaptation co-exists with offline adaptation for evolution.

During the last decades, performance of available hardware resources constantly increased (Moore, 1965), which enabled the assignment of more and more complex tasks to software systems. As one consequence, the inherent complexity of these software systems also increases, influencing all phases of their lifecycle. The concept of component orientation (CO) (Szyperski, 1999) allows the development of software systems in a modular way through functional decomposition. Administration and maintenance of software systems are addressed by the vision of autonomic computing (AC) (Horn, 2001), based on the idea to assign low level administrative tasks to the system itself. With mKernel an AC-infrastructure for component oriented enterprise applications is provided, based on the enterprise Java bean (EJB) standard, version 3.0 (DeMichiel and Keith, 2006). In contrast to existing approaches, the main advantage of mKernel lies within its standard compliance, not prescribing any additional guidelines for the development of applications to enable their autonomous management. It is realized as plugin for an existing container, not requiring any adjustment of the underlying implementation. Moreover, it provides a very fine grained interface for inspection and manipulation of the managed system, taking the specifics of the supported standard into account. Within this paper we present the opportunities provided by mKernel to control a managed system.