Karan Tongay

Alvi Mahadi, Neil A. Ernst, Karan Tongay

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 paper we demonstrate a simple example of how design mining works. We then show how conclusion stability is poor on different artifact types and different projects. We show two techniques -- augmentation and context specificity -- that greatly improve the conclusion stability and cross-project relevance of design mining. Our new approach achieves AUC of 0.88 on within dataset classification and 0.80 on the cross-dataset classification task.

Alvi Mahadi, Karan Tongay, Neil A. Ernst

Being able to identify software discussions that are primarily about design, which we call design mining, can improve documentation and maintenance of software systems. Existing design mining approaches have good classification performance using natural language processing (NLP) techniques, but the conclusion stability of these approaches is generally poor. A classifier trained on a given dataset of software projects has so far not worked well on different artifacts or different datasets. In this study, we replicate and synthesize these earlier results in a meta-analysis. We then apply recent work in transfer learning for NLP to the problem of design mining. However, for our datasets, these deep transfer learning classifiers perform no better than less complex classifiers. We conclude by discussing some reasons behind the transfer learning approach to design mining.