Can ChatGPT Support Developers? An Empirical Evaluation of Large Language Models for Code Generation
February 18, 2024 Β· Declared Dead Β· π IEEE Working Conference on Mining Software Repositories
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Authors
Kailun Jin, Chung-Yu Wang, Hung Viet Pham, Hadi Hemmati
arXiv ID
2402.11702
Category
cs.SE: Software Engineering
Cross-listed
cs.AI,
cs.LG
Citations
35
Venue
IEEE Working Conference on Mining Software Repositories
Last Checked
4 months ago
Abstract
Large language models (LLMs) have demonstrated notable proficiency in code generation, with numerous prior studies showing their promising capabilities in various development scenarios. However, these studies mainly provide evaluations in research settings, which leaves a significant gap in understanding how effectively LLMs can support developers in real-world. To address this, we conducted an empirical analysis of conversations in DevGPT, a dataset collected from developers' conversations with ChatGPT (captured with the Share Link feature on platforms such as GitHub). Our empirical findings indicate that the current practice of using LLM-generated code is typically limited to either demonstrating high-level concepts or providing examples in documentation, rather than to be used as production-ready code. These findings indicate that there is much future work needed to improve LLMs in code generation before they can be integral parts of modern software development.
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