Developer discussions range from in-person hallway chats to comment chains on bug reports. Being able to identify discussions that touch on software design would be helpful in documentation and refactoring software. Design mining is the application of machine learning techniques to correctly label a given discussion artifact, such as a pull request, as pertaining (or not) to design. In this work we demonstrate a simple example of how design mining works. We first replicate an existing state-of-the-art design mining study to show how conclusion stability is poor on different artifact types and different projects. Then we introduce two techniques—augmentation and context specificity—that greatly improve the conclusion stability and cross-project relevance of design mining. Our new approach achieves AUC-ROC of 0.88 on within dataset classification and 0.84 on the cross-dataset classification task. / Graduate
| Identifer | oai:union.ndltd.org:uvic.ca/oai:dspace.library.uvic.ca:1828/12672 |
| Date | 11 February 2021 |
| Creators | Mahadi, Alvi |
| Contributors | Ernst, Neil A. |
| Source Sets | University of Victoria |
| Language | English, English |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
| Rights | Available to the World Wide Web |
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