Naturally Mining Facts for Queries From Their Search Results
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2020-09-22T12:17:04Z
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Abstract
When a new bug report is received, developers usually need to reproduce the bug and
perform code reviews to find the cause, a process that can be tedious and time consuming. A
tool for ranking all the source files with respect to how likely they are to contain the cause of
the bug would enable developers to narrow down their search and improve productivity.
This paper introduces anadaptive ranking approach that leverages project knowledge
through functional decomposition of source code, API descriptions oflibrary components,
the bug-fixing history, the code change history, and the file dependency graph. Given a bug
report, the rankingscore of each source file is computed as a weighted combination of an
array of features, where the weights are trained automatically onpreviously solved bug
reports using a learning-to-rank technique. We evaluate the ranking system on six large scale
open source Java projects, using the before-fix version of the project for every bug report.
The experimental results show that the learning-to-rank approach outperforms three recent
state-of-the-art methods. In particular, our method makes correct recommendations within
the top10 ranked source files for over 70 percent of the bug reports in the Eclipse Platform
and Tomcat projects.
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SACHIN GOUR, 1NZ17MCA29