Traceability in the Wild: Automatically Augmenting Incomplete Trace Links

TL;DR

This paper presents an automated method to identify and augment missing trace links between commits and issues in software projects, improving traceability completeness using machine learning techniques.

Contribution

It introduces a novel classifier that leverages process and text features to detect missing issue tags and augment trace links in open source projects.

Findings

Achieved 96% recall in recommending missing issue links

Attained over 89% precision in augmenting existing trace links

Improved traceability completeness in open source projects

Abstract

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%