BugScope: Learn to Find Bugs Like Human

• URL: http://arxiv.org/abs/2507.15671v1
• Date: Mon, 21 Jul 2025 14:34:01 GMT
• Title: BugScope: Learn to Find Bugs Like Human
• Authors: Jinyao Guo, Chengpeng Wang, Dominic Deluca, Jinjie Liu, Zhuo Zhang, Xiangyu Zhang,
• Abstract summary: BugScope emulates how human auditors learn new bug patterns from representative examples and apply that knowledge during code auditing.<n>Our evaluation on a dataset of 40 real-world bugs drawn from 21 widely-used open-source projects demonstrates that BugScope achieves 87.04% precision.<n>Further testing on large-scale open-source systems, including the Linux kernel, uncovered 141 previously unknown bugs.
• Score: 9.05553442116139
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Detecting software bugs remains a fundamental challenge due to the extensive diversity of real-world defects. Traditional static analysis tools often rely on symbolic workflows, which restrict their coverage and hinder adaptability to customized bugs with diverse anti-patterns. While recent advances incorporate large language models (LLMs) to enhance bug detection, these methods continue to struggle with sophisticated bugs and typically operate within limited analysis contexts. To address these challenges, we propose BugScope, an LLM-driven multi-agent system that emulates how human auditors learn new bug patterns from representative examples and apply that knowledge during code auditing. Given a set of examples illustrating both buggy and non-buggy behaviors, BugScope synthesizes a retrieval strategy to extract relevant detection contexts via program slicing and then constructs a tailored detection prompt to guide accurate reasoning by the LLM. Our evaluation on a curated dataset of 40 real-world bugs drawn from 21 widely-used open-source projects demonstrates that BugScope achieves 87.04% precision and 90.00% recall, surpassing state-of-the-art industrial tools by 0.44 in F1 score. Further testing on large-scale open-source systems, including the Linux kernel, uncovered 141 previously unknown bugs, of which 78 have been fixed and 7 confirmed by developers, highlighting BugScope's substantial practical impact.

Related papers

• BugGen: A Self-Correcting Multi-Agent LLM Pipeline for Realistic RTL Bug Synthesis [1.9291502706655312]
  We introduce BugGen, a first of its kind, fully autonomous, multi-agent pipeline to generate, insert, and validate functional bugs in RTL.<n> BugGen partitions modules, selects mutation targets via a closed-loop agentic architecture, and employs iterative refinement and rollback mechanisms.<n> evaluated across five OpenTitan IP blocks, BugGen produced 500 unique bugs with 94% functional accuracy and achieved a throughput of 17.7 validated bugs per hour-over five times faster than typical manual expert insertion.
  arXiv  Detail & Related papers  (2025-06-12T09:02:20Z)
• A Comparative Study of Fuzzers and Static Analysis Tools for Finding Memory Unsafety in C and C++ [24.60320701097142]
  We present an empirical analysis of five static analyzers and 13 fuzzers, applied to over 100 known security vulnerabilities in C/C++ programs.<n>We find that both techniques discover different types of bugs, but there are clear winners for each.
  arXiv  Detail & Related papers  (2025-05-28T07:22:29Z)
• RepoAudit: An Autonomous LLM-Agent for Repository-Level Code Auditing [8.846583362353169]
  RepoAudit is an autonomous repository-level code auditing agent.<n>It detects 40 true bugs across 15 real-world benchmark projects with a precision of 78.43%.<n>Also, it detects 185 new bugs in high-profile projects, among which 174 have been confirmed or fixed.
  arXiv  Detail & Related papers  (2025-01-30T05:56:30Z)
• Error Classification of Large Language Models on Math Word Problems: A Dynamically Adaptive Framework [64.83955753606443]
  Math Word Problems serve as a crucial benchmark for evaluating Large Language Models' reasoning abilities.<n>Current error classification methods rely on static and predefined categories.<n>We introduce MWPES-300K, a comprehensive dataset containing 304,865 error samples.
  arXiv  Detail & Related papers  (2025-01-26T16:17:57Z)
• 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)
• Leveraging Large Language Models for Efficient Failure Analysis in Game Development [47.618236610219554]
  This paper proposes a new approach to automatically identify which change in the code caused a test to fail. The method leverages Large Language Models (LLMs) to associate error messages with the corresponding code changes causing the failure. Our approach reaches an accuracy of 71% in our newly created dataset, which comprises issues reported by developers at EA over a period of one year.
  arXiv  Detail & Related papers  (2024-06-11T09:21:50Z)
• A Comprehensive Library for Benchmarking Multi-class Visual Anomaly Detection [52.228708947607636]
  This paper proposes a comprehensive visual anomaly detection benchmark, ADer, which is a modular framework for new methods.<n>The benchmark includes multiple datasets from industrial and medical domains, implementing fifteen state-of-the-art methods and nine comprehensive metrics.<n>We objectively reveal the strengths and weaknesses of different methods and provide insights into the challenges and future directions of multi-class visual anomaly detection.
  arXiv  Detail & Related papers  (2024-06-05T13:40:07Z)
• FineWAVE: Fine-Grained Warning Verification of Bugs for Automated Static Analysis Tools [18.927121513404924]
  Automated Static Analysis Tools (ASATs) have evolved over time to assist in detecting bugs. Previous research efforts have explored learning-based methods to validate the reported warnings. We propose FineWAVE, a learning-based approach that verifies bug-sensitive warnings at a fine-grained granularity.
  arXiv  Detail & Related papers  (2024-03-24T06:21:35Z)
• DebugBench: Evaluating Debugging Capability of Large Language Models [80.73121177868357]
  DebugBench is a benchmark for Large Language Models (LLMs) It covers four major bug categories and 18 minor types in C++, Java, and Python. We evaluate two commercial and four open-source models in a zero-shot scenario.
  arXiv  Detail & Related papers  (2024-01-09T15:46:38Z)
• What Happens When We Fuzz? Investigating OSS-Fuzz Bug History [0.9772968596463595]
  We analyzed 44,102 reported issues made public by OSS-Fuzz prior to March 12, 2022. We identified the bug-contributing commits to estimate when the bug containing code was introduced, and measure the timeline from introduction to detection to fix.
  arXiv  Detail & Related papers  (2023-05-19T05:15:36Z)
• Using Developer Discussions to Guide Fixing Bugs in Software [51.00904399653609]
  We propose using bug report discussions, which are available before the task is performed and are also naturally occurring, avoiding the need for additional information from developers. We demonstrate that various forms of natural language context derived from such discussions can aid bug-fixing, even leading to improved performance over using commit messages corresponding to the oracle bug-fixing commits.
  arXiv  Detail & Related papers  (2022-11-11T16:37:33Z)

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.