Security Defect Detection via Code Review: A Study of the OpenStack and Qt Communities
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Abstract (arXiv)
Background: Despite the widespread use of automated security defect detection tools, software projects still contain many security defects that could result in serious damage. Such tools are largely context-insensitive and may not cover all possible scenarios in testing potential issues, which makes them susceptible to missing complex security defects. Hence, thorough detection entails a synergistic cooperation between these tools and human-intensive detection techniques, including code review. Code review is widely recognized as a crucial and effective practice for identifying security defects. Aim: This work aims to empirically investigate security defect detection through code review. Method: To this end, we conducted an empirical study by analyzing code review comments derived from four projects in the OpenStack and Qt communities. Through manually checking 20,995 review comments obtained by keyword-based search, we identified 614 comments as security-related. Results: Our results show that (1) security defects are not prevalently discussed in code review, (2) more than half of the reviewers provided explicit fixing strategies/solutions to help developers fix security defects, (3) developers tend to follow reviewers' suggestions and action the changes, (4) Not worth fixing the defect now and Disagreement between the developer and the reviewer are the main causes for not resolving security defects. Conclusions: Our research results demonstrate that (1) software security practices should combine manual code review with automated detection tools, achieving a more comprehensive coverage to identifying and addressing security defects, and (2) promoting appropriate standardization of practitioners' behaviors during code review remains necessary for enhancing software security.
Key facts. Cited by 4 papers in the map (data through 13 Oct 2026). First posted on arXiv 5 Jul 2023.
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Citing papers per year: 2024: 1; 2025: 2; 2026: 1.
4 papers.
- 2024An Insight into Security Code Review with LLMs: Capabilities, Obstacles, and Influential Factorscited by 6
- 2025Securing the AI Supply Chain: What Can We Learn From Developer-Reported Security Issues and Solutions of AI Projects?cited by 1
- 2025Improving Automated Secure Code Reviews: A Synthetic Dataset for Code Vulnerability Flawscited by 0
- 2026Agentic Repository Mining: A Multi-Task Evaluationcited by 0
- 2026Agentic Repository Mining: A Multi-Task Evaluation6 May 2026
- 2025Securing the AI Supply Chain: What Can We Learn From Developer-Reported Security Issues and Solutions of AI Projects?29 Dec 2025
- 2025Improving Automated Secure Code Reviews: A Synthetic Dataset for Code Vulnerability Flaws22 Apr 2025
- 2024An Insight into Security Code Review with LLMs: Capabilities, Obstacles, and Influential Factors29 Jan 2024