Automated Topic Naming

Abstract Software repositories provide a deluge of software artifacts to analyze. Researchers have attempted to summarize, categorize, and relate these artifacts by using semi- unsupervised machine-learning algorithms, such as Latent Dirichlet Allocation (LDA). LDA is used for concept and topic analysis to suggest candidate word-lists or topics that describe and relate software artifacts. However, these word-lists and topics are difficult to interpret in the absence of meaningful summary labels. Current attempts to interpret topics assume