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Exploring ChatGPT's code summarization capabilities: an empirical study
0
Zitationen
4
Autoren
2024
Jahr
Abstract
Various automated code summarization techniques enhance the efficiency and accuracy of code annotations, enabling succinct natural language comments for code snippets. Recently, large language models (LLMs) have significantly improved natural language processing tasks. Among them, ChatGPT, based on the GPT-3.5 architecture, has gained widespread attention in academia and industry. Previous studies have also tested the ability of ChatGPT in code summarization, designing heuristic questions to explore an appropriate prompt that can guide ChatGPT to generate comments. In contrast, we have designed a more targeted and adaptive suggestion word strategy to study the impact of prompt design on model generation summary. Additionally, we have made extensive data fine-tuning to enhance ChatGPT's ability in code summarization tasks. The experimental results demonstrate that our prompt strategy has significantly improved the quality of code summaries generated by ChatGPT compared to previous studies, but still falls short of the SOTA model.
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