Intelligent software debugging: A reinforcement learning approach for detecting the shortest crashing scenarios

The Quality Assurance (QA) team verifies software for months before its release decisions. Nevertheless, some crucial bugs remain undetected in manual testing. These bugs would make the system unusable on field, thus merchant loses money then manufacturer loses its customers. Thus, automatic softwar...

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Veröffentlicht in:	Expert systems with applications Jg. 198; S. 116722
Hauptverfasser:	Durmaz, Engin, Tümer, M. Borahan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Elsevier Ltd 15.07.2022 Elsevier BV
Schlagworte:	Algorithms Crashes Debugging Delta debugging Detected goal catalyst Exploring crashes by prioritized sweeping Exploring crashes by SARSA Machine learning Quality assurance Reinforcement learning in automated bug detection Software testing Reinforcement learning in automated bug detection Exploring crashes by SARSA Exploring crashes by prioritized sweeping Delta debugging Detected goal catalyst
ISSN:	0957-4174, 1873-6793
Online-Zugang:	Volltext
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