How Robust are LLM-Generated Library Imports? An Empirical Study using Stack Overflow

• URL: http://arxiv.org/abs/2507.10818v1
• Date: Mon, 14 Jul 2025 21:35:29 GMT
• Title: How Robust are LLM-Generated Library Imports? An Empirical Study using Stack Overflow
• Authors: Jasmine Latendresse, SayedHassan Khatoonabadi, Emad Shihab,
• Abstract summary: We conduct an empirical study of six Large Language Models (LLMs)<n>We analyze the types of libraries they import, the characteristics of those libraries, and the extent to which the recommendations are usable out of the box.<n>Our results show that LLMs predominantly favour third-party libraries over standard ones, and often recommend mature, popular, and permissively licensed dependencies.
• Score: 3.076436880934678
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Software libraries are central to the functionality, security, and maintainability of modern code. As developers increasingly turn to Large Language Models (LLMs) to assist with programming tasks, understanding how these models recommend libraries is essential. In this paper, we conduct an empirical study of six state-of-the-art LLMs, both proprietary and open-source, by prompting them to solve real-world Python problems sourced from Stack Overflow. We analyze the types of libraries they import, the characteristics of those libraries, and the extent to which the recommendations are usable out of the box. Our results show that LLMs predominantly favour third-party libraries over standard ones, and often recommend mature, popular, and permissively licensed dependencies. However, we also identify gaps in usability: 4.6% of the libraries could not be resolved automatically due to structural mismatches between import names and installable packages, and only two models (out of six) provided installation guidance. While the generated code is technically valid, the lack of contextual support places the burden of manually resolving dependencies on the user. Our findings offer actionable insights for both developers and researchers, and highlight opportunities to improve the reliability and usability of LLM-generated code in the context of software dependencies.

Related papers

• LLMs Love Python: A Study of LLMs' Bias for Programming Languages and Libraries [15.140178992235123]
  Large Language Models (LLMs) play an increasing role in code generation.<n>This study provides the first in-depth investigation into LLM preferences for programming languages and libraries used when generating code.<n>Our findings reveal that LLMs heavily favour Python when solving language-agnostic problems.
  arXiv  Detail & Related papers  (2025-03-21T14:29:35Z)
• SWE-Fixer: Training Open-Source LLMs for Effective and Efficient GitHub Issue Resolution [56.9361004704428]
  Large Language Models (LLMs) have demonstrated remarkable proficiency across a variety of complex tasks.<n>SWE-Fixer is a novel open-source framework designed to effectively and efficiently resolve GitHub issues.<n>We assess our approach on the SWE-Bench Lite and Verified benchmarks, achieving competitive performance among open-source models.
  arXiv  Detail & Related papers  (2025-01-09T07:54:24Z)
• Codellm-Devkit: A Framework for Contextualizing Code LLMs with Program Analysis Insights [9.414198519543564]
  We present codellm-devkit (hereafter, CLDK'), an open-source library that significantly simplifies the process of performing program analysis. CLDK offers developers an intuitive and user-friendly interface, making it incredibly easy to provide rich program analysis context to code LLMs.
  arXiv  Detail & Related papers  (2024-10-16T20:05:59Z)
• What's Wrong with Your Code Generated by Large Language Models? An Extensive Study [80.18342600996601]
  Large language models (LLMs) produce code that is shorter yet more complicated as compared to canonical solutions. We develop a taxonomy of bugs for incorrect codes that includes three categories and 12 sub-categories, and analyze the root cause for common bug types. We propose a novel training-free iterative method that introduces self-critique, enabling LLMs to critique and correct their generated code based on bug types and compiler feedback.
  arXiv  Detail & Related papers  (2024-07-08T17:27:17Z)
• ML-Bench: Evaluating Large Language Models and Agents for Machine Learning Tasks on Repository-Level Code [76.84199699772903]
  ML-Bench is a benchmark rooted in real-world programming applications that leverage existing code repositories to perform tasks. To evaluate both Large Language Models (LLMs) and AI agents, two setups are employed: ML-LLM-Bench for assessing LLMs' text-to-code conversion within a predefined deployment environment, and ML-Agent-Bench for testing autonomous agents in an end-to-end task execution within a Linux sandbox environment.
  arXiv  Detail & Related papers  (2023-11-16T12:03:21Z)
• Evaluating In-Context Learning of Libraries for Code Generation [35.57902679044737]
  Large Language Models (LLMs) exhibit a high degree of code generation and comprehension capability. Recent work has shown that large proprietary LLMs can learn novel library usage in-context from demonstrations.
  arXiv  Detail & Related papers  (2023-11-16T07:37:25Z)
• LILO: Learning Interpretable Libraries by Compressing and Documenting Code [71.55208585024198]
  We introduce LILO, a neurosymbolic framework that iteratively synthesizes, compresses, and documents code. LILO combines LLM-guided program synthesis with recent algorithmic advances in automated from Stitch. We find that AutoDoc boosts performance by helping LILO's synthesizer to interpret and deploy learned abstractions.
  arXiv  Detail & Related papers  (2023-10-30T17:55:02Z)
• CodeTF: One-stop Transformer Library for State-of-the-art Code LLM [72.1638273937025]
  We present CodeTF, an open-source Transformer-based library for state-of-the-art Code LLMs and code intelligence. Our library supports a collection of pretrained Code LLM models and popular code benchmarks. We hope CodeTF is able to bridge the gap between machine learning/generative AI and software engineering.
  arXiv  Detail & Related papers  (2023-05-31T05:24:48Z)
• SequeL: A Continual Learning Library in PyTorch and JAX [50.33956216274694]
  SequeL is a library for Continual Learning that supports both PyTorch and JAX frameworks. It provides a unified interface for a wide range of Continual Learning algorithms, including regularization-based approaches, replay-based approaches, and hybrid approaches. We release SequeL as an open-source library, enabling researchers and developers to easily experiment and extend the library for their own purposes.
  arXiv  Detail & Related papers  (2023-04-21T10:00:22Z)
• An Empirical Study of Library Usage and Dependency in Deep Learning Frameworks [12.624032509149869]
  pytorch, Caffe, and Scikit-learn are the most frequent combination in 18% and 14% of the projects. The developer uses two or three dl libraries in the same projects and tends to use different multiple dl libraries in both the same function and the same files.
  arXiv  Detail & Related papers  (2022-11-28T19:31:56Z)
• Code Librarian: A Software Package Recommendation System [65.05559087332347]
  We present a recommendation engine called Librarian for open source libraries. A candidate library package is recommended for a given context if: 1) it has been frequently used with the imported libraries in the program; 2) it has similar functionality to the imported libraries in the program; 3) it has similar functionality to the developer's implementation, and 4) it can be used efficiently in the context of the provided code.
  arXiv  Detail & Related papers  (2022-10-11T12:30:05Z)
• MARLlib: A Scalable and Efficient Multi-agent Reinforcement Learning Library [82.77446613763809]
  We present MARLlib, a library designed to offer fast development for multi-agent tasks and algorithm combinations. MARLlib can effectively disentangle the intertwined nature of the multi-agent task and the learning process of the algorithm. The library's source code is publicly accessible on GitHub.
  arXiv  Detail & Related papers  (2022-10-11T03:11:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.