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<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/vjelrzmdcvaydenhyicylh7bwa" style="color: black;">Proceedings of the 11th Working Conference on Mining Software Repositories - MSR 2014</a>
Static analysis (SA) tools that find bugs by inferring programmer beliefs (e.g., FindBugs) are commonplace in today's software industry. While they find a large number of actual defects, they are often plagued by high rates of alerts that a developer would not act on (unactionable alerts) because they are incorrect, do not significantly affect program execution, etc. High rates of unactionable alerts decrease the utility of static analysis tools in practice. We present a method for<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/2597073.2597100">doi:10.1145/2597073.2597100</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/msr/HanamTHL14.html">dblp:conf/msr/HanamTHL14</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/qeln4vvumrgkvazh4emkabf724">fatcat:qeln4vvumrgkvazh4emkabf724</a> </span>
more »... ng actionable and unactionable alerts by finding alerts with similar code patterns. To do so, we create a feature vector based on code characteristics at the site of each SA alert. With these feature vectors, we use machine learning techniques to build an actionable alert prediction model that is able to classify new SA alerts. We evaluate our technique on three subject programs using the FindBugs static analysis tool and the Faultbench benchmark methodology. For a developer inspecting the top 5% of all alerts for three sample projects, our approach is able to identify 57 of 211 actionable alerts, which is 38 more than the FindBugs priority measure. Combined with previous actionable alert identification techniques, our method finds 75 actionable alerts in the top 5%, which is four more actionable alerts (a 6% improvement) than previous actionable alert identification techniques.
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