1 research outputs found
Traceability in the Wild: Automatically Augmenting Incomplete Trace Links
Software and systems traceability is widely accepted as an essential element
for supporting many software development tasks. Today's version control systems
provide inbuilt features that allow developers to tag each commit with one or
more issue ID, thereby providing the building blocks from which project-wide
traceability can be established between feature requests, bug fixes, commits,
source code, and specific developers. However, our analysis of six open source
projects showed that on average only 60% of the commits were linked to specific
issues. Without these fundamental links the entire set of project-wide links
will be incomplete, and therefore not trustworthy. In this paper we address the
fundamental problem of missing links between commits and issues. Our approach
leverages a combination of process and text-related features characterizing
issues and code changes to train a classifier to identify missing issue tags in
commit messages, thereby generating the missing links. We conducted a series of
experiments to evaluate our approach against six open source projects and
showed that it was able to effectively recommend links for tagging issues at an
average of 96% recall and 33% precision. In a related task for augmenting a set
of existing trace links, the classifier returned precision at levels greater
than 89% in all projects and recall of 50%Comment: ICSE 201