Predicting Good Configurations for GitHub and Stack Overflow Topic Models

TL;DR

This paper studies how to optimally configure LDA for topic modeling on GitHub and Stack Overflow data, revealing that different corpora require tailored parameters and proposing a method to predict good configurations for new datasets.

Contribution

It provides a comprehensive analysis of LDA parameter settings for software repository texts and introduces a predictive approach for optimal configurations on unseen corpora.

Findings

Popular LDA parameter rules are not universally applicable.

GitHub and Stack Overflow corpora require different configurations.

Good configurations can be reliably predicted for new corpora.

Abstract

Software repositories contain large amounts of textual data, ranging from source code comments and issue descriptions to questions, answers, and comments on Stack Overflow. To make sense of this textual data, topic modelling is frequently used as a text-mining tool for the discovery of hidden semantic structures in text bodies. Latent Dirichlet allocation (LDA) is a commonly used topic model that aims to explain the structure of a corpus by grouping texts. LDA requires multiple parameters to work well, and there are only rough and sometimes conflicting guidelines available on how these parameters should be set. In this paper, we contribute (i) a broad study of parameters to arrive at good local optima for GitHub and Stack Overflow text corpora, (ii) an a-posteriori characterisation of text corpora related to eight programming languages, and (iii) an analysis of corpus feature importance via per-corpus LDA configuration. We find that (1) popular rules of thumb for topic modelling parameter configuration are not applicable to the corpora used in our experiments, (2) corpora sampled from GitHub and Stack Overflow have different characteristics and require different configurations to achieve good model fit, and (3) we can predict good configurations for unseen corpora reliably. These findings support researchers and practitioners in efficiently determining suitable configurations for topic modelling when analysing textual data contained in software repositories.