Automated topic naming: supporting cross-project analysis of software maintenance activities

TitleAutomated topic naming: supporting cross-project analysis of software maintenance activities
Publication TypeJournal Article
Year of Publication2012
AuthorsHindle, A, Ernst, NA, Godfrey, MW, Mylopoulos, J
Secondary TitleEmpirical Software Engineering
ISSN Number1573-7616
KeywordsLDA, maxdb, mysql, postgresql, topics

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 manual labelling and do not use domain-specific knowledge to improve, contextualize, or describe results for the developers. We propose a solution: automated labelled topic extraction. Topics are extracted using LDA from commit-log comments recovered from source control systems. These topics are given labels from a generalizable cross-project taxonomy, consisting of non-functional requirements. Our approach was evaluated with experiments and case studies on three large-scale Relational Database Management System (RDBMS) projects: MySQL, PostgreSQL and MaxDB. The case studies show that labelled topic extraction can produce appropriate, context-sensitive labels that are relevant to these projects, and provide fresh insight into their evolving software development activities.

Short TitleEmpir Software Eng
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