Sifting through the Chaff: On Utilizing Execution Feedback for Ranking the Generated Code Candidates

Large Language Models (LLMs), such as GPT-4, StarCoder, and Code Llama, are transforming the way developers approach programming by automatically generating code based on given contexts, such as natural language descriptions or incomplete surrounding code. Despite advancements, generating syntactica...

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Veröffentlicht in:	IEEE/ACM International Conference on Automated Software Engineering : [proceedings] S. 229 - 241
Hauptverfasser:	Sun, Zhihong, Wan, Yao, Li, Jia, Zhang, Hongyu, Jin, Zhi, Li, Ge, Lyu, Chen
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	ACM 27.10.2024
Schlagworte:	Benchmark testing Cause effect analysis Code Generation Code Ranking Codes Execution Feedback Large language models Multitasking Natural languages Security Software Software engineering Source coding
ISSN:	2643-1572
Online-Zugang:	Volltext
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