arXiv subjects

Baharin A. Jodat

Publications and source records attributed to Baharin A. Jodat.

2 recordsLinked to original sources

arXiv2026

Synthesizing Behavioural Models of CPS Using Automata Learning and Statistical Machine Learning

Inferring behavioural models from system executions is essential for supporting formal verification and analysis of complex, heterogeneous cyber-physical systems (CPS). Automata learning provides an effective way to infer state machine models from system executions. However, CPS inputs and outputs often consist of numeric time-series data, while automata learning algorithms assume inputs over a finite symbolic alphabet. As a result, raw numeric data must first be abstracted into a finite set of symbols. In this article, we present MELA, a passive automata learning approach enhanced with machine learning to synthesize behavioural models from numeric time-series data generated by CPS. MELA systematically combines statistical machine learning with automata learning to automatically abstract raw numeric signals into interpretable intervals that are strongly correlated with system states. Specifically, MELA uses information-theoretic variable selection and decision-tree-based range abstraction to transform numeric traces into symbolic representations suitable for automata learning. We evaluate MELA on two CPS: a commercial network intrusion detection system developed by our industry partner, RabbitRun Technologies, and a publicly available industrial autopilot benchmark from the aerospace domain. Compared with expertise-based numeric data abstraction, MELA reduces the number of states and transitions in the learned state machines by 49.20% on average, while improving accuracy by 41.71% on average. Furthermore, the learned state machines support system-level requirement verification and help practitioners explore behaviours that are not explicit in the system requirements. We make our implementation and experimental data available online. Keywords: Automata learning, Cyber-physical systems, Behavioural model synthesis, Decision trees, Model checking, Intrusion detection, Simulink.

cs.SE↗

arXiv2026

Automated Test Validators for Flaky Cyber-Physical System Simulators: Approach and Evaluation

Simulation-based testing of cyber-physical systems (CPS) is costly due to the time-consuming execution of CPS simulators. In addition, CPS simulators may be flaky, leading to inconsistent test outcomes and requiring repeated test re-execution for reliable test verdicts. Many test inputs within the input space of CPS may not effectively exercise the behaviour of the system under test (SUT) -- for instance, those that violate system preconditions, exceed operational design domain (ODD) limits, or represent inherently safe scenarios. In this article, we propose to use test validators to filter out such test inputs before execution. We describe two methods for generating test validators: one using genetic programming (GP) that employs well-known spectrum-based fault localization (SBFL) ranking formulas, namely Ochiai, Tarantula, and Naish, as fitness functions; and the other using decision trees (DT) and decision rules (DR). We evaluate our test validators through case studies in the domains of aerospace, networking and autonomous driving. We show that test validators generated using GP with Ochiai are significantly more accurate than those generated using GP with Tarantula and Naish or using DT or DR. Moreover, this accuracy advantage remains even when accounting for the flakiness of the simulator. We further show that our test validators generated by GP with Ochiai are robust against flakiness with only 4% average variation in their accuracy results across four different network and autonomous-driving systems with flaky behaviours. Finally, we show that, on average, 88.7% of the assertions inferred by our approach align or overlap with requirements precondition violations, ODD-limit violations, and nominal safe conditions extracted from technical standards and empirical results in the literature.

cs.SE↗