Automatically Labelled Software Topic Model

Abstract

Public software repositories (SR) maintain a massive amount of valuable data offering opportunities to support software engineering (SE) tasks. Researchers have applied information retrieval techniques in mining software repositories. Topic models are one of these techniques. However, this technique does not give an interpretation nor labels to the extracted topics and it requires manual analysis to identify them. Some approaches were proposed to automatically label the topics using tags in SR, but they do not consider the existence of spam-tags and they have difficulties to scale to large tag space. This article introduces a novel approach called automatically labelled software topic model (AL-STM) that labels the topics based on observed tags in SR. It mitigates the shortcomings of manual and automatic labelling of topics in SE. AL-STM is implemented using 22K GitHub projects and evaluated in a SE task (tag recommending) against the currently used techniques. The empirical results suggest that AL-STM is more robust in terms of MAP and nDCG, and more scalable to large tag space.