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Self-adaptation solutions need to periodically monitor, reason about, and adapt a running system. The adaptation step involves generating an adaptation strategy and applying it to the running system whenever an anomaly arises. In this article, we argue that rather than generating individual adaptation strategies, the goal should be to adapt the control logic of the running system in such a way that the system itself would learn how to steer clear of future anomalies, without triggering self-adaptation too frequently. Although the need for adaptation is never eliminated, especially noting the uncertain and evolving environment of complex systems, reducing the frequency of adaptation interventions is advantageous for various reasons, such as to increase performance and to make a running system more robust. We instantiate and empirically examine the preceding idea for software-defined networking—a key enabling technology for modern data centers and Internet of Things applications. Using genetic programming (GP), we propose a self-adaptation solution that continuously learns and updates the control constructs in the data-forwarding logic of a software-defined network. Our evaluation, performed using open source synthetic and industrial data, indicates that compared to a baseline adaptation technique that attempts to generate individual adaptations, our GP-based approach is more effective in resolving network congestion, and further, it reduces the frequency of adaptation interventions over time. In addition, we show that for networks with the same topology, reusing over larger networks the knowledge that is learned on smaller networks leads to significant improvements in the performance of our GP-based adaptation approach. Finally, we compare our approach against a standard data-forwarding algorithm from the network literature, demonstrating that our approach significantly reduces packet loss. [ABSTRACT FROM AUTHOR]

The authors discuss common control software maintenance tasks and tool capabilities based on existing research, and demonstrate the implementation of these capabilities in the 4diac IDE. However, there is limited empirical research on the usability and utility of model-based development environments for visual programming languages. The MODELS conference series is the premier venue for model-based software and systems engineering covering all aspects of modeling, from languages and methods to tools and applications. [Extracted from the article]

Estimating worst-case execution time (WCET) is an important activity at early design stages of real-time systems. Based on WCET estimates, engineers make design and implementation decisions to ensure that task executions always complete before their specified deadlines. However, in practice, engineers often cannot provide precise point WCET estimates and prefer to provide plausible WCET ranges. Given a set of real-time tasks with such ranges, we provide an automated technique to determine for what WCET values the system is likely to meet its deadlines and, hence, operate safely with a probabilistic guarantee. Our approach combines a search algorithm for generating worst-case scheduling scenarios with polynomial logistic regression for inferring probabilistic safe WCET ranges. We evaluated our approach by applying it to three industrial systems from different domains and several synthetic systems. Our approach efficiently and accurately estimates probabilistic safe WCET ranges within which deadlines are likely to be satisfied with a high degree of confidence. [ABSTRACT FROM AUTHOR]

Configuration in the domain of Integrated Control Systems (ICS) is largely manual, laborious, and error prone. In this article, we propose a model-based configuration approach that provides automation support for reducing configuration effort and the likelihood of configuration errors in the ICS domain. We ground our approach on component-based specifications of ICS families. We then develop a configuration algorithm using constraint satisfaction techniques over finite domains to generate products that are consistent with respect to their ICS family specifications. We reason about the termination and consistency of our configuration algorithm analytically. We evaluate the effectiveness of our configuration approach by applying it to a real subsea oil production system. Specifically, we have rebuilt a number of existing verified product configurations of our industry partner. Our experience shows that our approach can automatically infer up to 50% of the configuration decisions, and reduces the complexity of making configuration decisions. [ABSTRACT FROM AUTHOR]

Certifying safety-critical software and ensuring its safety requires checking the conformance between safety requirements and design. Increasingly, the development of safety-critical software relies on modeling, and the System Modeling Language (SysML) is now commonly used in many industry sectors. Inspecting safety conformance by comparing design models against safety requirements requires safety inspectors to browse through large models and is consequently time consuming and error-prone. To address this, we have devised a mechanism to establish traceability between (functional) safety requirements and SysML design models to extract design slices (model fragments) that filter out irrelevant details but keep enough context information for the slices to be easy to inspect and understand. In this article, we report on a controlled experiment assessing the impact of the traceability and slicing mechanism on inspectors' conformance decisions and effort. Results show a significant decrease in effort and an increase in decisions' correctness and level of certainty. [ABSTRACT FROM AUTHOR]