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Software repositories contain large amounts of textual data, ranging fromsource code comments and issue descriptions to questions, answers, and commentson Stack Overflow. To make sense of this textual data, topic modelling isfrequently used as a text-mining tool for the discovery of hidden semanticstructures in text bodies. Latent Dirichlet allocation (LDA) is a commonly usedtopic 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 andsometimes conflicting guidelines available on how these parameters should beset. In this paper, we contribute (i) a broad study of parameters to arrive atgood local optima for GitHub and Stack Overflow text corpora, (ii) ana-posteriori characterisation of text corpora related to eight programminglanguages, and (iii) an analysis of corpus feature importance via per-corpusLDA configuration. We find that (1) popular rules of thumb for topic modellingparameter configuration are not applicable to the corpora used in ourexperiments, (2) corpora sampled from GitHub and Stack Overflow have differentcharacteristics and require different configurations to achieve good model fit,and (3) we can predict good configurations for unseen corpora reliably. Thesefindings support researchers and practitioners in efficiently determiningsuitable configurations for topic modelling when analysing textual datacontained in software repositories.

Predicting Good Configurations for GitHub and Stack Overflow Topic Models