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BUGMINER: Software Reliability Analysis Via Data Mining of Bug Reports

Leon Li Wu; Boyi Xie; Gail E. Kaiser; Rebecca Passonneau

Title:
BUGMINER: Software Reliability Analysis Via Data Mining of Bug Reports
Author(s):
Wu, Leon Li
Xie, Boyi
Kaiser, Gail E.
Passonneau, Rebecca
Date:
Type:
Technical reports
Department:
Computer Science
Permanent URL:
Series:
Columbia University Computer Science Technical Reports
Part Number:
CUCS-024-11
Publisher:
Department of Computer Science, Columbia University
Publisher Location:
New York
Abstract:
Software bugs reported by human users and automatic error reporting software are often stored in some bug tracking tools (e.g., Bugzilla and Debbugs). These accumulated bug reports may contain valuable information that could be used to improve the quality of the bug reporting, reduce the quality assurance effort and cost, analyze software reliability, and predict future bug report trend. In this paper, we present BUGMINER, a tool that is able to derive useful information from historic bug report database using data mining, use these information to do completion check and redundancy check on a new or given bug report, and to estimate the bug report trend using statistical analysis. Our empirical studies of the tool using several real-world bug report repositories show that it is effective, easy to implement, and has relatively high accuracy despite low quality data.
Subject(s):
Computer science
Item views:
580
Metadata:
text | xml

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