Using groupings of static analysis alerts to identify files likely to contain field failures

Mark S. Sherriff, Sarah Smith Heckman, J. Michael Lake, Laurie A. Williams
<span title="">2007</span> <i title="ACM Press"> The 6th Joint Meeting on European software engineering conference and the ACM SIGSOFT symposium on the foundations of software engineering companion papers - ESEC-FSE companion &#39;07 </i> &nbsp;
In this paper, we propose a technique for leveraging historical field failure records in conjunction with automated static analysis alerts to determine which alerts or sets of alerts are predictive of a field failure. Our technique uses singular value decomposition to generate groupings of static analysis alert types, which we call alert signatures, that have been historically linked to field failure-prone files in previous releases of a software system. The signatures can be applied to sets of
more &raquo; ... alerts from a current build of a software system. Files that have a matching alert signature are identified as having similar static analysis alert characteristics to files with known field failures in a previous release of the system. We performed a case study involving an industrial software system at IBM and found three distinct alert signatures that could be applied to the system. We found that 50% of the field failures reported since the last static analysis run could be discovered by examining the 10% of the files and static analysis alerts indicated by these three alert signatures. The remaining failures were either not detected by a signature which could be an indication of a new type of error in the field, or they were on areas of the code where no static analysis alerts were detected.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="">doi:10.1145/1295014.1295042</a> <a target="_blank" rel="external noopener" href="">fatcat:pxgac6lubna3bksifsavzmxbum</a> </span>
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