The Foundation Cracks: A Comprehensive Study on Bugs and Testing Practices in LLM Libraries

• URL: http://arxiv.org/abs/2506.12320v1
• Date: Sat, 14 Jun 2025 03:00:36 GMT
• Title: The Foundation Cracks: A Comprehensive Study on Bugs and Testing Practices in LLM Libraries
• Authors: Weipeng Jiang, Xiaoyu Zhang, Xiaofei Xie, Jiongchi Yu, Yuhan Zhi, Shiqing Ma, Chao Shen,
• Abstract summary: Large Language Model (LLM) libraries have emerged as the foundational infrastructure powering today's AI revolution.<n>Despite their critical role in the LLM ecosystem, these libraries face frequent quality issues and bugs that threaten the reliability of AI systems built upon them.<n>We present the first comprehensive empirical investigation into bug characteristics and testing practices in modern LLM libraries.
• Score: 37.57398329330302
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Model (LLM) libraries have emerged as the foundational infrastructure powering today's AI revolution, serving as the backbone for LLM deployment, inference optimization, fine-tuning, and production serving across diverse applications. Despite their critical role in the LLM ecosystem, these libraries face frequent quality issues and bugs that threaten the reliability of AI systems built upon them. To address this knowledge gap, we present the first comprehensive empirical investigation into bug characteristics and testing practices in modern LLM libraries. We examine 313 bug-fixing commits extracted across two widely-adopted LLM libraries: HuggingFace Transformers and vLLM.Through rigorous manual analysis, we establish comprehensive taxonomies categorizing bug symptoms into 5 types and root causes into 14 distinct categories.Our primary discovery shows that API misuse has emerged as the predominant root cause (32.17%-48.19%), representing a notable transition from algorithm-focused defects in conventional deep learning frameworks toward interface-oriented problems. Additionally, we examine 7,748 test functions to identify 7 distinct test oracle categories employed in current testing approaches, with predefined expected outputs (such as specific tensors and text strings) being the most common strategy. Our assessment of existing testing effectiveness demonstrates that the majority of bugs escape detection due to inadequate test cases (41.73%), lack of test drivers (32.37%), and weak test oracles (25.90%). Drawing from these findings, we offer some recommendations for enhancing LLM library quality assurance.

Related papers

• SpecTool: A Benchmark for Characterizing Errors in Tool-Use LLMs [77.79172008184415]
  SpecTool is a new benchmark to identify error patterns in LLM output on tool-use tasks. We show that even the most prominent LLMs exhibit these error patterns in their outputs. Researchers can use the analysis and insights from SPECTOOL to guide their error mitigation strategies.
  arXiv  Detail & Related papers  (2024-11-20T18:56:22Z)
• Exploring and Lifting the Robustness of LLM-powered Automated Program Repair with Metamorphic Testing [31.327835928133535]
  Large language model-powered Automated Program Repair (LAPR) techniques have achieved state-of-the-art bug-fixing performance.<n>It is crucial to conduct robustness testing on LAPR techniques before their practical deployment.<n>We propose MT-LAPR, a Metamorphic Testing framework exclusively for LAPR techniques.
  arXiv  Detail & Related papers  (2024-10-10T01:14:58Z)
• Exploring Automatic Cryptographic API Misuse Detection in the Era of LLMs [60.32717556756674]
  This paper introduces a systematic evaluation framework to assess Large Language Models in detecting cryptographic misuses. Our in-depth analysis of 11,940 LLM-generated reports highlights that the inherent instabilities in LLMs can lead to over half of the reports being false positives. The optimized approach achieves a remarkable detection rate of nearly 90%, surpassing traditional methods and uncovering previously unknown misuses in established benchmarks.
  arXiv  Detail & Related papers  (2024-07-23T15:31:26Z)
• 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)
• Large-scale, Independent and Comprehensive study of the power of LLMs for test case generation [11.517293765116307]
  Unit testing is essential for software reliability, yet manual test creation is time-consuming and often neglected.<n>This study presents the first large-scale empirical evaluation of LLM-generated unit tests at the class level.
  arXiv  Detail & Related papers  (2024-06-28T20:38:41Z)
• AutoDetect: Towards a Unified Framework for Automated Weakness Detection in Large Language Models [95.09157454599605]
  Large Language Models (LLMs) are becoming increasingly powerful, but they still exhibit significant but subtle weaknesses.<n>Traditional benchmarking approaches cannot thoroughly pinpoint specific model deficiencies.<n>We introduce a unified framework, AutoDetect, to automatically expose weaknesses in LLMs across various tasks.
  arXiv  Detail & Related papers  (2024-06-24T15:16:45Z)
• An Exploratory Study on Using Large Language Models for Mutation Testing [32.91472707292504]
  Large Language Models (LLMs) have shown great potential in code-related tasks but their utility in mutation testing remains unexplored. This paper investigates the performance of LLMs in generating effective mutations to their usability, fault detection potential, and relationship with real bugs. We find that compared to existing approaches, LLMs generate more diverse mutations that are behaviorally closer to real bugs.
  arXiv  Detail & Related papers  (2024-06-14T08:49:41Z)
• OR-Bench: An Over-Refusal Benchmark for Large Language Models [65.34666117785179]
  Large Language Models (LLMs) require careful safety alignment to prevent malicious outputs.<n>This study proposes a novel method for automatically generating large-scale over-refusal datasets.<n>We introduce OR-Bench, the first large-scale over-refusal benchmark.
  arXiv  Detail & Related papers  (2024-05-31T15:44:33Z)
• To Err is Machine: Vulnerability Detection Challenges LLM Reasoning [8.602355712876815]
  We present a challenging code reasoning task: vulnerability detection.<n>State-of-the-art (SOTA) models reported only 54.5% Balanced Accuracy in our vulnerability detection evaluation.<n>New models, new training methods, or more execution-specific pretraining data may be needed to conquer vulnerability detection.
  arXiv  Detail & Related papers  (2024-03-25T21:47:36Z)
• LLMs Cannot Reliably Identify and Reason About Security Vulnerabilities (Yet?): A Comprehensive Evaluation, Framework, and Benchmarks [17.522223535347905]
  Large Language Models (LLMs) have been suggested for use in automated vulnerability repair, but benchmarks showing they can consistently identify security-related bugs are lacking. We develop SecLLMHolmes, a fully automated evaluation framework that performs the most detailed investigation to date on whether LLMs can reliably identify and reason about security-related bugs.
  arXiv  Detail & Related papers  (2023-12-19T20:19:43Z)
• Large Language Models are Few-shot Testers: Exploring LLM-based General Bug Reproduction [14.444294152595429]
  The number of tests added in open source repositories due to issues was about 28% of the corresponding project test suite size. We propose LIBRO, a framework that uses Large Language Models (LLMs), which have been shown to be capable of performing code-related tasks. Our evaluation of LIBRO shows that, on the widely studied Defects4J benchmark, LIBRO can generate failure reproducing test cases for 33% of all studied cases.
  arXiv  Detail & Related papers  (2022-09-23T10:50:47Z)

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.