A Comparison of Bug Finding Tools for Java
ISSRE '04 Proceedings of the 15th International Symposium on Software Reliability Engineering
ACM SIGPLAN Notices
Proceedings of the 28th international conference on Software engineering
Thorough static analysis of device drivers
Proceedings of the 1st ACM SIGOPS/EuroSys European Conference on Computer Systems 2006
Automated Defect Prevention: Best Practices in Software Management
Automated Defect Prevention: Best Practices in Software Management
Design and code reviews in the age of the internet
Communications of the ACM - Enterprise information integration: and other tools for merging data
Proceedings of the 19th international symposium on Software testing and analysis
Software Engineering
Introduction to Software Process Improvement
Introduction to Software Process Improvement
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Peer code review is a cost-effective software defect detection technique. Tool assisted code review is a form of peer code review, which can improve both quality and quantity of reviews. However, there is a significant amount of human effort involved even in tool based code reviews. Using static analysis tools, it is possible to reduce the human effort by automating the checks for coding standard violations and common defect patterns. Towards this goal, we propose a tool called Review Bot for the integration of automatic static analysis with the code review process. Review Bot uses output of multiple static analysis tools to publish reviews automatically. Through a user study, we show that integrating static analysis tools with code review process can improve the quality of code review. The developer feedback for a subset of comments from automatic reviews shows that the developers agree to fix 93% of all the automatically generated comments. There is only 14.71% of all the accepted comments which need improvements in terms of priority, comment message, etc. Another problem with tool assisted code review is the assignment of appropriate reviewers. Review Bot solves this problem by generating reviewer recommendations based on change history of source code lines. Our experimental results show that the recommendation accuracy is in the range of 60%-92%, which is significantly better than a comparable method based on file change history.