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Is ChatGPT a Good Software Librarian? An Exploratory Study on the Use of ChatGPT for Software Library Recommendations

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arxiv 2408.05128 v1 pith:OHFHSXVU submitted 2024-08-09 cs.SE cs.AIcs.LG

Is ChatGPT a Good Software Librarian? An Exploratory Study on the Use of ChatGPT for Software Library Recommendations

classification cs.SE cs.AIcs.LG
keywords chatgptsoftwarelibrariesdeveloperslibrariancodeeffectivenesslibrary
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Correct Code, Vulnerable Dependencies: A Large Scale Measurement Study of LLM-Specified Library Versions

    cs.SE 2026-05 conditional novelty 8.0

    LLMs frequently specify library versions with known CVEs in generated code (36-56% of tasks), show low compatibility (20-63%), and converge on the same risky versions across models.

  2. Library Hallucinations in LLM-Generated Code: A Risk Analysis Grounded in Developer Queries

    cs.SE 2025-09 unverdicted novelty 7.0

    A study of seven LLMs finds that realistic prompt variations such as one-character misspellings trigger library hallucinations in up to 26% of cases, fabricated names in up to 99%, and time-based prompts in up to 85%,...

  3. Bridging Generation and Training: A Systematic Review of Quality Issues in LLMs for Code

    cs.SE 2026-05 accept novelty 6.0

    A review of 114 studies creates taxonomies for code and data quality issues, formalizes 18 propagation mechanisms from training data defects to LLM-generated code defects, and synthesizes detection and mitigation techniques.

  4. A Study of LLMs' Preferences for Libraries and Programming Languages

    cs.SE 2025-03 unverdicted novelty 6.0

    Empirical study of eight LLMs finds overuse of popular libraries like NumPy in up to 45% of unnecessary cases and strong default preference for Python even when suboptimal.