WCET and Priority Assignment Analysis of Real-Time Systems using Search and Machine Learning

Real-time systems have become indispensable for human life as they are used in numerous industries, such as vehicles, medical devices, and satellite systems. These systems are very sensitive to violations of their time constraints (deadlines), which can have catastrophic consequences. To verify whether the systems meet their time constraints, engineers perform schedulability analysis from early stages and throughout development. However, there are challenges in obtaining precise results from schedulability analysis due to estimating the worst-case execution times (WCETs) and assigning optimal priorities to tasks. Estimating WCET is an important activity at early design stages of real-time systems. Based on such 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 a precise point of WCET estimates and they prefer to provide plausible WCET ranges. Task priority assignment is an important decision, as it determines the order of task executions and it has a substantial impact on schedulability results. It thus requires finding optimal priority assignments so that tasks not only complete their execution but also maximize the safety margins from their deadlines. Optimal priority values increase the tolerance of real-time systems to unexpected overheads in task executions so that they can still meet their deadlines. However, it is a hard problem to find optimal priority assignments because their evaluation relies on uncertain WCET values and complex engineering constraints must be accounted for. This dissertation proposes three approaches to estimate WCET and assign optimal priorities at design stages. Combining a genetic algorithm and logistic regression, we first suggest an automatic approach to infer safe WCET ranges with a probabilistic guarantee based on the worst-case scheduling scenarios. We then introduce an extended approach to account for weakly hard real-time systems with an industrial schedule simulator. We evaluate our approaches by applying them to industrial systems from different domains and several synthetic systems. The results suggest that they are possible to estimate probabilistic safe WCET ranges efficiently and accurately so the deadline constraints are likely to be satisfied with a high degree of confidence. Moreover, we propose an automated technique that aims to identify the best possible priority assignments in real-time systems. The approach deals with multiple objectives regarding safety margins and engineering constraints using a coevolutionary algorithm. Evaluation with synthetic and industrial systems shows that the approach significantly outperforms both a baseline approach and solutions defined by practitioners. All the solutions in this dissertation scale to complex industrial systems for offline analysis within an acceptable time, i.e., at most 27 hours

Tools and Algorithms for the Construction and Analysis of Systems

This open access two-volume set constitutes the proceedings of the 27th International Conference on Tools and Algorithms for the Construction and Analysis of Systems, TACAS 2021, which was held during March 27 – April 1, 2021, as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2021. The conference was planned to take place in Luxembourg and changed to an online format due to the COVID-19 pandemic. The total of 41 full papers presented in the proceedings was carefully reviewed and selected from 141 submissions. The volume also contains 7 tool papers; 6 Tool Demo papers, 9 SV-Comp Competition Papers. The papers are organized in topical sections as follows: Part I: Game Theory; SMT Verification; Probabilities; Timed Systems; Neural Networks; Analysis of Network Communication. Part II: Verification Techniques (not SMT); Case Studies; Proof Generation/Validation; Tool Papers; Tool Demo Papers; SV-Comp Tool Competition Papers

Integrierte modell- und simulationsbasierte Entwicklung zur dynamischen Bewertung automobiler Elektrik/Elektronik-Architekturen

Die Automobilbranche befindet sich seit einigen Jahren im Wandel. Trends wie autonomes Fahren, Konnektivität, smarte Mobilität sowie die Elektrifizierung führen zu einer drastischen Erhöhung der Fahrzeugkomplexität. Diese Komplexität muss durch die zugrunde liegende Elektrik/Elektronik-Architektur (E/E-Architektur) beherrscht werden und ruft unmittelbare neue Herausforderungen an den Entwicklungsprozess hervor. Design-Entscheidungen der E/E-Architektur haben maßgeblichen Einfluss auf das Verhalten von Fahrzeugfunktionen und umgekehrt. Daher müssen sie möglichst frühzeitig analysiert und evaluiert werden, um kostspielige Fehlerkorrekturen in späten Entwicklungsphasen zu minimieren. Eine frühzeitige Einbindung von Simulationsmethoden ist dabei zentral. Die modellbasierte Architekturentwicklung und Simulation sind jedoch weitestgehend getrennt voneinander laufende Prozesse. Dies erschwert eine effiziente Analyse sowie Bewertung der bidirektionalen Abhängigkeiten zwischen Architektur und Verhalten. Um diese Schwächen zu adressieren, wird in dieser Arbeit eine integrierte Methodik zur modell- und simulationsbasierten Entwicklung von E/E-Architekturen vorgestellt, die sich in drei Teile gliedert. Es werden zunächst neue Methoden zur architekturzentrierten Verhaltensmodellierung eingeführt. Eine nachfolgende Synthese generiert daraus ein Simulationsmodell, welches automatisiert mehrere Abstraktionsebenen der E/E-Architektur miteinander verknüpft und so zu einer ganzheitlichen Betrachtung beiträgt. Mithilfe des integrierten Ansatzes wird zusätzlich ein Konzept entwickelt, das es gestattet, mehrere Architekturvarianten automatisiert bzgl. statischen und dynamischen Metriken gegenüberzustellen. Die Konzepte werden in das in der Automobilindustrie etablierte E/E-Architekturwerkzeug PREEvision® integriert, umgesetzt und anhand mehrerer Anwendungsfälle evaluiert