Automatic Matching Release Notes and Source Code by Generating Summary for Software Change

To quickly locate the source code that maps to a specific change described in change history, establishing traceability links between release notes and source code is a necessary task. Current works on the traceability link recovery can be used to find out source code changes which are of higher textual similarities with the release note. However, these approaches rely on consistency of the text used in artifacts at various abstraction levels, and the completeness of text descriptions. In this paper, we propose to leverage source code change information for improving the accuracy of release note to source code traceability recovery tasks. In order to reduce the complexity of link recovery, our approach first performs change impact analysis to cluster the source code changes for the same purpose as a virtual class. After that, our approach employs a natural language generation algorithm to generate readable summary sentence for each virtual class. The traceability links are built between release notes and clusters of program entities by computing the linguistic similarity of sentences. We conduct case studies on 26 releases of 3 popular softwares to evaluate the approach, and the results indicate that our proposed method can improve the accuracy of traceability link recovery compared to other IR-based techniques.