Leveraging Large Language Model for Automatic Patch Correctness Assessment

Automated Program Repair (APR) techniques have shown more and more promising results in fixing real-world bugs. Despite the effectiveness, APR techniques still face an overfitting problem: a generated patch can be incorrect although it passes all tests. It is time-consuming to manually evaluate the...

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Veröffentlicht in:	IEEE transactions on software engineering Jg. 50; H. 11; S. 2865 - 2883
Hauptverfasser:	Zhou, Xin, Xu, Bowen, Kim, Kisub, Han, DongGyun, Nguyen, Hung Huu, Le-Cong, Thanh, He, Junda, Le, Bach, Lo, David
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.11.2024 IEEE Computer Society
Schlagworte:	Automatic patch correctness assessment Automation Codes Computer bugs Feature extraction in-context learning Labels Large language models large language models of code Manuals Task analysis Training
ISSN:	0098-5589, 1939-3520
Online-Zugang:	Volltext
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