Designing bug detection rules for fewer false alarms
Jaechang Nam, Song Wang, Xi Yuan, Lin Tan · 2018
One of the challenging issues of the existing static analysis tools is the high false alarm rate. To address the false alarm issue, we design bug detection rules by learning from a large number of real bugs from open-source projects from GitHub. Specifically, we build a framework that learns and refines bug detection rules for fewer false positives. Based on the framework, we implemented ten patterns, six of which are new ones to existing tools. To evaluate the framework, we implemented a static analysis tool, FeeFin, based on the framework with the ten bug detection rules and applied the tool for 1,800 open-source projects in GitHub. The 57 detected bugs by FeeFin has been confirmed by developers as true positives and 44 bugs out of the detected bugs were actually fixed.