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

TL;DR

This study evaluates ChatGPT's effectiveness in recommending Python libraries for developers, revealing strengths in recommending popular options but also highlighting issues with licensing clarity and library usability.

Contribution

The paper provides an empirical assessment of ChatGPT's library recommendation capabilities, identifying key areas for improvement in licensing transparency and usability.

Findings

ChatGPT recommends third-party libraries 10% more often than humans.

14.2% of recommended libraries have restrictive licenses.

6.5% of libraries do not work out of the box.

Abstract

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.