BUGMINER: Software Reliability Analysis Via Data Mining of Bug Reports Leon Wu Boyi Xie Gail Kaiser Rebecca Passonneau Department of Computer Science Columbia University New York, NY 10027 USA fleon,xie,kaiser,
[email protected] Abstract formation that could be used to improve the quality of the bug reporting, reduce the cost of quality assurance, analyze Software bugs reported by human users and automatic software reliability, and predict future bug report trend. One error reporting software are often stored in some bug track- of the challenges in bug reporting is that the bug reports are ing tools (e.g., Bugzilla and Debbugs). These accumulated often incomplete (e.g., missing data fields such as product bug reports may contain valuable information that could version or operating system details). Another challenge is be used to improve the quality of the bug reporting, reduce that there are often many duplicate bug reports for the same the quality assurance effort and cost, analyze software re- bug. Software developers or testers normally have to re- liability, and predict future bug report trend. In this paper, view these redundant bug reports manually, which is time- we present BUGMINER, a tool that is able to derive useful consuming and cost inefficient. information from historic bug report database using data We developed a tool named BUGMINER that is able to mining, use these information to do completion check and derive useful information from historic bug reports using redundancy check on a new or given bug report, and to es- data mining techniques, including machine learning (e.g., timate the bug report trend using statistical analysis.