{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T11:35:51Z","timestamp":1791459351341,"version":"4.3.4"},"reference-count":61,"publisher":"Association for Computing Machinery (ACM)","issue":"ISSTA","license":[{"start":{"date-parts":[[2025,6,22]],"date-time":"2025-06-22T00:00:00Z","timestamp":1750550400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. ACM Softw. Eng."],"published-print":{"date-parts":[[2025,6,22]]},"abstract":"<jats:p>\n                    Testing Autonomous Driving Systems (ADSs) is crucial for ensuring their safety, reliability, and performance. Despite numerous testing methods available that can generate diverse and challenging scenarios to uncover potential vulnerabilities, these methods often treat ADS as a black-box, primarily focusing on identifying system-level failures like collisions or near-misses without pinpointing the specific modules responsible for these failures. This lack of root causes understanding for the failures hinders effective debugging and subsequent system repair. Furthermore, current approaches often fall short in generating violations that adequately test the individual modules of an ADS from a system-level perspective, such as perception, prediction, planning, and control. To bridge this gap, we introduce\n                    <jats:italic toggle=\"yes\">MoDitector<\/jats:italic>\n                    , a root-cause-aware testing method for ADS that generates safety-critical scenarios specifically designed to expose weaknesses in targeted ADS modules. Unlike existing approaches,\n                    <jats:italic toggle=\"yes\">MoDitector<\/jats:italic>\n                    not only produces scenarios that lead to violations but also pinpoints the specific module responsible for each failure. Specifically, our approach introduces\n                    <jats:italic toggle=\"yes\">Module-Specific Oracles<\/jats:italic>\n                    to automatically detect module-level errors and identify the root-cause module responsible for system-level violations. To effectively generate module-specific failures, we propose a module-directed testing strategy that integrates\n                    <jats:italic toggle=\"yes\">Module-Specific Feedback<\/jats:italic>\n                    and\n                    <jats:italic toggle=\"yes\">Adaptive Scenario Generation<\/jats:italic>\n                    to guide the testing process. We evaluated\n                    <jats:italic toggle=\"yes\">MoDitector<\/jats:italic>\n                    across four critical ADS modules and four representative testing scenarios. The results demonstrate that\n                    <jats:italic toggle=\"yes\">MoDitector<\/jats:italic>\n                    can effectively and efficiently generate scenarios in which failures can be attributed to specific targeted modules. In total,\n                    <jats:italic toggle=\"yes\">MoDitector<\/jats:italic>\n                    generated 216.7 expected scenarios, significantly outperforming the best baseline, which identified only 79.0 scenarios. Our approach represents a significant innovation in ADS testing by focusing on the identification and rectification of module-specific errors within the system, moving beyond conventional black-box failure detection.\n                  <\/jats:p>","DOI":"10.1145\/3728876","type":"journal-article","created":{"date-parts":[[2025,6,22]],"date-time":"2025-06-22T10:52:56Z","timestamp":1750589576000},"page":"137-158","source":"Crossref","is-referenced-by-count":8,"title":["MoDitector: Module-Directed Testing for Autonomous Driving Systems"],"prefix":"10.1145","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1219-0732","authenticated-orcid":false,"given":"Renzhi","family":"Wang","sequence":"first","affiliation":[{"name":"University of Alberta, Edmonton, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8982-1483","authenticated-orcid":false,"given":"Mingfei","family":"Cheng","sequence":"additional","affiliation":[{"name":"Singapore Management University, Singapore, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1288-6502","authenticated-orcid":false,"given":"Xiaofei","family":"Xie","sequence":"additional","affiliation":[{"name":"Singapore Management University, Singapore, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1583-7570","authenticated-orcid":false,"given":"Yuan","family":"Zhou","sequence":"additional","affiliation":[{"name":"Zhejiang Sci-Tech University, Singapore, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8621-2420","authenticated-orcid":false,"given":"Lei","family":"Ma","sequence":"additional","affiliation":[{"name":"University of Tokyo, Tokyo, Japan"},{"name":"University of Alberta, Edmonton, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,6,22]]},"reference":[{"key":"e_1_3_1_2_2","unstructured":"[n. d.]. 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