Less is More: Supporting Developers in Vulnerability Detection during Code Review
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Abstract (arXiv)
Reviewing source code from a security perspective has proven to be a difficult task. Indeed, previous research has shown that developers often miss even popular and easy-to-detect vulnerabilities during code review. Initial evidence suggests that a significant cause may lie in the reviewers' mental attitude and common practices. In this study, we investigate whether and how explicitly asking developers to focus on security during a code review affects the detection of vulnerabilities. Furthermore, we evaluate the effect of providing a security checklist to guide the security review. To this aim, we conduct an online experiment with 150 participants, of which 71% report to have three or more years of professional development experience. Our results show that simply asking reviewers to focus on security during the code review increases eight times the probability of vulnerability detection. The presence of a security checklist does not significantly improve the outcome further, even when the checklist is tailored to the change under review and the existing vulnerabilities in the change. These results provide evidence supporting the mental attitude hypothesis and call for further work on security checklists' effectiveness and design. Data and materials: https://doi.org/10.5281/zenodo.6026291
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Feb 2022.
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Citing papers per year: 2023: 1; 2024: 4; 2025: 0; 2026: 1.
6 papers.
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- 2024A Survey of Source Code Representations for Machine Learning-Based Cybersecurity Taskscited by 4
- 2026AgenticSCR: An Autonomous Agentic Secure Code Review for Immature Vulnerabilities Detectioncited by 2
- 2024Unintentional Security Flaws in Code: Automated Defense via Root Cause Analysiscited by 0
- 2026AgenticSCR: An Autonomous Agentic Secure Code Review for Immature Vulnerabilities Detection27 Jan 2026
- 2024Unintentional Security Flaws in Code: Automated Defense via Root Cause Analysis30 Aug 2024
- 2024Automatic Data Labeling for Software Vulnerability Prediction Models: How Far Are We?25 Jul 2024
- 2024A Survey of Source Code Representations for Machine Learning-Based Cybersecurity Tasks15 Mar 2024
- 2024CodeAgent: Autonomous Communicative Agents for Code Review3 Feb 2024
- 2023An Unbiased Transformer Source Code Learning with Semantic Vulnerability Graph17 Apr 2023