Timing Debugging for Cyber-Physical Systems

Roy, Debayan; Hobbs, Clara; Anderson, James H.; Caccamo, Marco; Chakraborty, Samarjit

doi:10.23919/date51398.2021.9474012

Cited by 13 publications

(1 citation statement)

References 12 publications

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“…To deal with such safety-related challenges, there is an increasing interest in adopting agile development paradigms within the CPS safety-critical domains [6,7] to identify hazards and elicit safety requirements iteratively [8]. Consequently, researchers proposed the usage of Digital-Twins 1 technologies to simulate and test CPSs in a diversified set of scenarios [9,10,11,12,13] to support testing automation [14,15], regression testing [12,16], and debugging [17,18] activities. In this context, simulation-based testing has been suggested as a promising direction to improve the SDC testing practices [19,20,21] because simulation environments enable efficient test execution, reproducible results, and testing under critical conditions [22].…”

Section: Introductionmentioning

confidence: 99%

Cost-effective Simulation-based Test Selection in Self-driving Cars Software with SDC-Scissor

Birchler

Ganz

Khatiri

et al. 2022

2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)

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Simulation platforms facilitate the continuous development of complex systems such as self-driving cars (SDCs). However, previous results on testing SDCs using simulations have shown that most of the automatically generated tests do not strongly contribute to establishing confidence in the quality and reliability of the SDC. Therefore, those tests can be characterized as "uninformative", and running them generally means wasting precious computational resources. We address this issue with SDC-Scissor, a framework that leverages Machine Learning to identify simulation-based tests that are unlikely to detect faults in the SDC software under test and skip them before their execution. Consequently, by filtering out those tests, SDC-Scissor reduces the number of long-running simulations to execute and drastically increases the cost-effectiveness of simulation-based testing of SDCs software. Our evaluation concerning two large datasets and around 12'000 tests showed that SDC-Scissor achieved a higher classification F1-score (between 47% and 90%) than a randomized baseline in identifying tests that lead to a fault and reduced the time spent running uninformative tests (speedup between 107% and 170%).

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