mlsanityData to 7 Oct 2026

Learning to Reduce False Positives in Analytic Bug Detectors

arXiv 2203.09907 ↗First posted 8 Mar 2022cs.SE cs.LGcited by 12

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Abstract (arXiv)

Due to increasingly complex software design and rapid iterative development, code defects and security vulnerabilities are prevalent in modern software. In response, programmers rely on static analysis tools to regularly scan their codebases and find potential bugs. In order to maximize coverage, however, these tools generally tend to report a significant number of false positives, requiring developers to manually verify each warning. To address this problem, we propose a Transformer-based learning approach to identify false positive bug warnings. We demonstrate that our models can improve the precision of static analysis by 17.5%. In addition, we validated the generalizability of this approach across two major bug types: null dereference and resource leak.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 14 of its 30 references that are in the map. First posted on arXiv 8 Mar 2022.

Builds on

14 of 30 references.

References · 14

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2020Language Models are Few-Shot Learnerscited by 32,199
  4. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  5. 2019HuggingFace's Transformers: State-of-the-art Natural Language Processingcited by 2,201
  6. 2020CodeBERT: A Pre-Trained Model for Programming and Natural Languagescited by 1,286
  7. 2019CodeSearchNet Challenge: Evaluating the State of Semantic Code Searchcited by 638
  8. 2020IntelliCode Compose: Code Generation Using Transformercited by 225
  9. 2018Automated Vulnerability Detection in Source Code Using Deep Representation Learningcited by 114
  10. 2018DeepBugs: A Learning Approach to Name-based Bug Detectioncited by 65
  11. 2021Towards Automating Code Review Activitiescited by 54
  12. 2021Traceability Transformed: Generating more Accurate Links with Pre-Trained BERT Modelscited by 20
2 more
  1. 2017End-to-End Prediction of Buffer Overruns from Raw Source Code via Neural Memory Networkscited by 10
  2. 2016Learning a Static Analyzer from Datacited by 7

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 3; 2024: 4; 2025: 2; 2026: 2.

12 papers.

  1. 2024Can Large Language Models Write Parallel Code?cited by 36
  2. 2024Performance-Aligned LLMs for Generating Fast Codecited by 20
  3. 2025Beyond Final Code: A Process-Oriented Error Analysis of Software Development Agents in Real-World GitHub Scenarioscited by 14
  4. 2023HPC-Coder: Modeling Parallel Programs using Large Language Modelscited by 11
  5. 2026Reducing False Positives in Static Bug Detection with LLMs: An Empirical Study in Industrycited by 5
  6. 2026Sifting the Noise: A Comparative Study of LLM Agents in Vulnerability False Positive Filteringcited by 5
  7. 2023Multi-Granularity Detector for Vulnerability Fixescited by 4
  8. 2024Generative transformations and patterns in LLM-native approaches for software verification and falsificationcited by 2
  9. 2024REDO: Execution-Free Runtime Error Detection for COding Agentscited by 2
  10. 2025Security Bug Report Prediction Within and Across Projects: A Comparative Study of BERT and Random Forestcited by 2
  11. 2022How to Find Actionable Static Analysis Warnings: A Case Study with FindBugscited by 1
  12. 2023Reducing the False Positive Rate Using Bayesian Inference in Autonomous Driving Perceptioncited by 0