MINING UNSTRUCTURED SOFTWARE REPOSITORIES USING IR MODELS
Stephen W. Thomas · 2012
MINING SOFTWARE REPOSITORIES, which is the process of analyzing the data re-lated to software development practices, is an emerging field which aims toaid development teams in their day to day tasks. However, data in many software repositories is currently unused because the data is unstructured, and therefore difficult to mine and analyze. Information Retrieval (IR) techniques, which were developed specifically to handle unstructured data, have recently been used by researchers to mine and analyze the unstructured data in software repositories, with some success. The main contribution of this thesis is the idea that the research and practice of using IR models to mine unstructured software repositories can be improved by going beyond the current state of affairs. First, we propose new applications of IR models to existing software engineering tasks. Specifically, we present a technique to prioritize test cases based on their IR similarity, giving highest priority to those test cases that are most dissimilar. In another new application of IR models, we empirically recover how developers use their mailing list while developing software. Next, we show how the use of advanced IR techniques can improve results. Using a framework for combining disparate IR models, we find that bug localization performance