Correlation Exploitation in Error Ranking

Static program checking tools can find many serious bugs in software, but due to analysis limitations they also frequently emit false error reports. Such false positives can easily render the error checker useless by hiding real errors amidst the false. Effective error report ranking schemes mitigate the problem of false positives by suppressing them during the report inspection process [17, 19, 20]. In this way, ranking techniques provide a complementary method to increasing the precision of the analysis results of a checking tool. A weakness of previous ranking schemes, however, is that they produce static rankings that do not adapt as reports are inspected, ignoring useful correlations amongst reports. This paper addresses this weakness with two main contributions. First, we observe that both bugs and false positives frequently cluster by code locality. We analyze clustering behavior in historical bug data from two large systems and show how clustering can be exploited to greatly improve error report ranking. Second, we present a general probabilistic technique for error ranking that (1) exploits correlation behavior amongst reports and (2) incorporates user feedback into the ranking process. In our results we observe a factor of 2-8 improvement over randomized ranking for error reports emitted by both intra-procedural and inter-procedural analysis tools.