Framework for evaluating code generation ability of large language models

• Published in: ETRI journal Vol. 46; no. 1; pp. 106 - 117
• Main Authors: Yeo, Sangyeop, Ma, Yu‐Seung, Kim, Sang Cheol, Jun, Hyungkook, Kim, Taeho
• Format: Journal Article
• Language: English
• Published: Electronics and Telecommunications Research Institute (ETRI) 01.02.2024; 한국전자통신연구원
• Subjects: code generation; evaluation metric; large language model; natural language processing; software engineering; 전자/정보통신공학
• ISSN: 1225-6463; 2233-7326
• DOI: 10.4218/etrij.2023-0357

Large language models (LLMs) have revolutionized various applications in natural language processing and exhibited proficiency in generating programming code. We propose a framework for evaluating the code generation ability of LLMs and introduce a new metric, pass‐ratio@n, which captures the granularity of accuracy according to the pass rate of test cases. The framework is intended to be fully automatic to handle the repetitive work involved in generating prompts, conducting inferences, and executing the generated codes. A preliminary evaluation focusing on the prompt detail, problem publication date, and difficulty level demonstrates the successful integration of our framework with the LeetCode coding platform and highlights the applicability of the pass‐ratio@n metric.