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Adaptively ranking alerts generated from automated static analysis
Static analysis tools are useful for finding common programming mistakes that often lead to field failures. However, static analysis tools regularly generate a high number of false positive alerts, requiring manual inspection by the developer to determine if an alert is an indication of a fault. The adaptive ranking model presented in this paper utilizes feedback from developers about inspected alerts in order to rank the remaining alerts by the likelihood that an alert is an indication of a fault. Alerts are ranked based on the homogeneity of populations of generated alerts, historical developer feedback in the form of suppressing false positives and fixing true positive alerts, and historical, application-specific data about the alert ranking factors.
Adaptively ranking alerts generated from automated static analysis
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