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Safety Analysis of CPSs Over-the-air Updates: Test Case Prioritization and Selection

Andrea Bombarda, Luca Carminati, Nunzio Marco Bisceglia, Mehrnoosh Askarpour, Sahar Kokaly, Ramesh S, Marsha Chećhik, Claudio Menghi · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.24475 · cited by 0 works (OpenAlex) · open-access PDF

Korean

자동차 분야에서, 그들은 제조업체가 차량에 물리적 접근없이 소프트웨어를 업데이트 할 수 있습니다. 그러나, OTA 업데이트는 가능한 소프트웨어 구성의 큰 숫자를 생성 할 수 있습니다. 예를 들어, 최종 사용자는 주어진 OTA 업데이트에 대한 테스트 사례 우선 순위를 지원하는 프레임 워크를 결정할 수 있습니다. 이러한 구성의 조합 폭발은 SW 테스트를 방해합니다, CPS의 오류를 감지하기위한 표준 절차.이 제한을 극복하기 위해, 우리는 CRISP (CRIticality-based Selection and Prioritization)를 제안합니다. 예를 들어, 최종 사용자는 OTA 업데이트의 맥락에서 주어진 OTA 업데이트에 대한 테스트 사례 우선 순위를 지원할 수 있습니다. 우리의 테스트 프레임 워크

Machine translation (Meta M2M100); the English original follows.

Abstract

Over-the-air (OTA) updates for Cyber-Physical Systems (CPS) allow engineers to modify their software wirelessly. In the automotive domain, they enable manufacturers to update software without physical access to the vehicle. However, OTA updates can generate a large number of possible software configurations. For example, end users may decide whether or not to install any given OTA updates. The combinatorial explosion of these configurations hampers SW testing, a standard practice for detecting errors in CPS. To overcome this limitation, we propose CRISP (CRIticality-based Selection and Prioritization), a novel framework supporting test case prioritization and selection in the context of OTA updates. Our framework leverages test case robustness, i.e., how far they are from violating the system requirements, to drive test case prioritization and selection. It enables engineers to rank test case executions from most to least critical, and to select only the critical test cases relevant to a given OTA update. We evaluated our approach using a representative SW product line consisting of seven different vehicles, 22 driving scenarios, and two software components. We considered 224 tests, requiring 59 hours of execution time. We assessed the effectiveness of CRISP across five OTA updates. We compared our solution with (a) a random test case prioritization and selection algorithm, and (b) AP-DISC, identified as the state-of-the-art solution in a recent empirical study on the subject. Our results show that CRISP outperforms our baselines in finding failure-revealing test cases, and that our selection procedure can be configured to find reasonable and practical tradeoffs among accuracy (percentage of failure-revealing test cases identified) and computational time (time required to find them).

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Topics: Selection (genetic algorithm), Computer science, Context (archaeology), Prioritization, Software, Regression testing

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