Continuous Exploration of Infinitely Configurable Cyber-Physical Systems for Sample-based Testing (Co-InCyTe)

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Today's software comprises up to thousands of configuration options to adjust to diverse requirements, contexts and platforms. Each (Boolean) option doubles the size of the configuration space which makes quality assurance of every individual configuration practically impossible. Sampling strategies bypass this issue by defining criteria and algorithms for selecting preferable small, yet representative subsets of configurations. Unfortunately, recent sampling approaches are not ready for the upcoming era of safety-critical cyber-physical systems (CPS): (1) they are limited to finite configuration spaces, whereas CPS are literally infinitely configurable, (2) they require a configuration model explicitly specifying the valid configuration space which is not available for CPS in practice, (3) they generate samples in a one-shot manner requiring complete knowledge of the configuration space in advance, %which is also infeasible for CPS, (4) they apply black-box selection criteria, whereas effective sampling of CPS is impossible without additional domain knowledge, e.g., non-functional properties. The proposed project contributes a novel sampling methodology comprehensively tackling challenges (1) -- (4). For (1), we propose a novel configuration model integrating two concepts: feature models extended to infinite configuration spaces and trained classifiers to handle particularly complicated configuration constraints. Concerning (2), we adapt techniques for extracting configuration models from sets of known configurations. To tackle (3), we interleave configuration-model extraction and sample selection for continuously exploring unknown configurations. Finally, concerning (4), we use further solution-space knowledge in form of behavioral models and apply family-based analysis to identify critical configurations.