MLDebugging: Towards Benchmarking Code Debugging Across Multi-Library Scenarios

• URL: http://arxiv.org/abs/2506.13824v1
• Date: Sun, 15 Jun 2025 13:02:59 GMT
• Title: MLDebugging: Towards Benchmarking Code Debugging Across Multi-Library Scenarios
• Authors: Jinyang Huang, Xiachong Feng, Qiguang Chen, Hanjie Zhao, Zihui Cheng, Jiesong Bai, Jingxuan Zhou, Min Li, Libo Qin,
• Abstract summary: We introduce a benchmark designed to assess challenges within multi-library Python code.<n>Specifically, ML Debugging encompasses 126 distinct Python libraries, covering a wide range of multi-library code issues.<n>We conduct a thorough evaluation of ML Debugging using both mainstream open-source and closed-source LLMs.
• Score: 12.394473121581843
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Code debugging is a crucial task in software engineering, which attracts increasing attention. While remarkable success has been made in the era of large language models (LLMs), current research still focuses on the simple no-library or single-library setting, ignoring the complex multi-library scenario in real-world applications. To address this limitation, we make the first attempt to introduce MLDebugging (Multi-Library Debugging), a comprehensive benchmark designed to assess debugging challenges within multi-library Python code. Specifically, MLDebugging encompasses 126 distinct Python libraries, covering a wide range of multi-library code issues, categorized into seven distinct types. Furthermore, we conduct a thorough evaluation of MLDebugging using both mainstream open-source and closed-source LLMs and highlight that current LLMs still struggle to correctly perform code debugging across multi-library scenarios. We hope this work can uncover the potential of LLMs in multi-library debugging scenario and offer insights for future research.

Related papers

• ToolScan: A Benchmark for Characterizing Errors in Tool-Use LLMs [77.79172008184415]
  TOOLSCAN is a new benchmark to identify error patterns in LLM output on tool-use tasks.<n>We show that even the most prominent LLMs exhibit these error patterns in their outputs.<n>Researchers can use these insights from TOOLSCAN to guide their error mitigation strategies.
  arXiv  Detail & Related papers  (2024-11-20T18:56:22Z)
• MdEval: Massively Multilingual Code Debugging [37.48700033342978]
  We propose the first massively multilingual debug benchmark, which includes 3.6K test samples of 18 programming languages.<n>We introduce the instruction corpora MDEVAL-INSTRUCT by injecting bugs into the correct multilingual queries and solutions.<n>Our experiments on MDEVAL reveal a notable performance gap between open-source models and closed-source LLMs.
  arXiv  Detail & Related papers  (2024-11-04T17:36:40Z)
• 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)
• Perplexed: Understanding When Large Language Models are Confused [3.4208414448496027]
  This paper introduces perplexed, a library for exploring where a language model is perplexed. We conducted a case study focused on Large Language Models (LLMs) for code generation using an additional tool we built to help with the analysis of code models called codetokenizer. We found that our studied code LLMs had their worst performance on coding structures where the code was not syntactically correct.
  arXiv  Detail & Related papers  (2024-04-09T22:03:39Z)
• InfiBench: Evaluating the Question-Answering Capabilities of Code Large Language Models [56.723509505549536]
  InfiBench is the first large-scale freeform question-answering (QA) benchmark for code to our knowledge. It comprises 234 carefully selected high-quality Stack Overflow questions that span across 15 programming languages. We conduct a systematic evaluation for over 100 latest code LLMs on InfiBench, leading to a series of novel and insightful findings.
  arXiv  Detail & Related papers  (2024-03-11T02:06:30Z)
• LDB: A Large Language Model Debugger via Verifying Runtime Execution Step-by-step [35.76881887942524]
  Large language models (LLMs) are leading significant progress in code generation. In this study, we introduce Large Language Model Debugger (LDB) LDB segments the programs into basic blocks and tracks the values of intermediate variables after each block throughout the runtime execution.
  arXiv  Detail & Related papers  (2024-02-25T00:56:27Z)
• If LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code Empowers Large Language Models to Serve as Intelligent Agents [81.60906807941188]
  Large language models (LLMs) are trained on a combination of natural language and formal language (code) Code translates high-level goals into executable steps, featuring standard syntax, logical consistency, abstraction, and modularity.
  arXiv  Detail & Related papers  (2024-01-01T16:51:20Z)
• 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)
• FollowBench: A Multi-level Fine-grained Constraints Following Benchmark for Large Language Models [79.62191017182518]
  FollowBench is a benchmark for Fine-grained Constraints Following Benchmark for Large Language Models. We introduce a Multi-level mechanism that incrementally adds a single constraint to the initial instruction at each increased level. By evaluating 13 popular LLMs on FollowBench, we highlight the weaknesses of LLMs in instruction following and point towards potential avenues for future work.
  arXiv  Detail & Related papers  (2023-10-31T12:32:38Z)
• Inference with Reference: Lossless Acceleration of Large Language Models [97.04200102556551]
  LLMA is an accelerator to speed up Large Language Model (LLM) inference with references. It is motivated by the observation that there are abundant identical text spans between the decoding result by an LLM and the reference that is available in many real world scenarios.
  arXiv  Detail & Related papers  (2023-04-10T09:55:14Z)

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.