Is ChatGPT a Good Software Librarian? An Exploratory Study on the Use of ChatGPT for Software Library Recommendations
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Abstract (arXiv)
Software libraries play a critical role in the functionality, efficiency, and maintainability of software systems. As developers increasingly rely on Large Language Models (LLMs) to streamline their coding processes, the effectiveness of these models in recommending appropriate libraries becomes crucial yet remains largely unexplored. In this paper, we assess the effectiveness of ChatGPT as a software librarian and identify areas for improvement. We conducted an empirical study using GPT-3.5 Turbo to generate Python code for 10,000 Stack Overflow questions. Our findings show that ChatGPT uses third-party libraries nearly 10% more often than human developers, favoring widely adopted and well-established options. However, 14.2% of the recommended libraries had restrictive copyleft licenses, which were not explicitly communicated by ChatGPT. Additionally, 6.5% of the libraries did not work out of the box, leading to potential developer confusion and wasted time. While ChatGPT can be an effective software librarian, it should be improved by providing more explicit information on maintainability metrics and licensing. We recommend that developers implement rigorous dependency management practices and double-check library licenses before integrating LLM-generated code into their projects.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 4 of its 42 references that are in the map. First posted on arXiv 9 Aug 2024.
Builds on
4 of 42 references.
References · 4
- 2023Can ChatGPT replace StackOverflow? A Study on Robustness and Reliability of Large Language Model Code Generationcited by 72
- 2024Can ChatGPT Support Developers? An Empirical Evaluation of Large Language Models for Code Generationcited by 11
- 2023A Preliminary Analysis on the Code Generation Capabilities of GPT-3.5 and Bard AI Models for Java Functionscited by 8
- 2024Evaluation of the Programming Skills of Large Language Modelscited by 2
Built on by
Citing papers per year: 2024: 1; 2025: 4; 2026: 2.
7 papers.
- 2025A Study of LLMs' Preferences for Libraries and Programming Languagescited by 20
- 2024A Comprehensive Framework for Evaluating API-oriented Code Generation in Large Language Modelscited by 2
- 2025Library Hallucinations in LLM-Generated Code: A Risk Analysis Grounded in Developer Queriescited by 1
- 2025HFuzzer: Testing Large Language Models for Package Hallucinations via Phrase-based Fuzzingcited by 1
- 2026Bridging Generation and Training: A Systematic Review of Quality Issues in LLMs for Codecited by 1
- 2025LibRec: Benchmarking Retrieval-Augmented LLMs for Library Migration Recommendationscited by 0
- 2026Skills That Don't Exist: A Large-Scale Study of Hallucinated Skill Recommendation in LLM Agentscited by 0
- 2026Skills That Don't Exist: A Large-Scale Study of Hallucinated Skill Recommendation in LLM Agents14 Jul 2026
- 2026Bridging Generation and Training: A Systematic Review of Quality Issues in LLMs for Code6 May 2026
- 2025HFuzzer: Testing Large Language Models for Package Hallucinations via Phrase-based Fuzzing28 Sep 2025
- 2025Library Hallucinations in LLM-Generated Code: A Risk Analysis Grounded in Developer Queries26 Sep 2025
- 2025LibRec: Benchmarking Retrieval-Augmented LLMs for Library Migration Recommendations13 Aug 2025
- 2025A Study of LLMs' Preferences for Libraries and Programming Languages21 Mar 2025
- 2024A Comprehensive Framework for Evaluating API-oriented Code Generation in Large Language Models23 Sep 2024