Title :
Mining software repositories using topic models
Author :
Thomas, Stephen W.
Author_Institution :
Software Anal. & Intell. Lab. (SAIL), Queen´´s Univ., Kingston, ON, Canada
Abstract :
Software repositories, such as source code, email archives, and bug databases, contain unstructured and unlabeled text that is difficult to analyze with traditional techniques. We propose the use of statistical topic models to automatically discover structure in these textual repositories. This discovered structure has the potential to be used in software engineering tasks, such as bug prediction and traceability link recovery. Our research goal is to address the challenges of applying topic models to software repositories.
Keywords :
data mining; program debugging; program diagnostics; software engineering; statistical analysis; bug databases; bug prediction; email archives; software engineering; software repository mining; source code; statistical topic model; textual repository; topic models; traceability link recovery; Adaptation models; Computational modeling; Data mining; Object oriented modeling; Resource management; Software; Software engineering; lda; mining software repositories; topic models;
Conference_Titel :
Software Engineering (ICSE), 2011 33rd International Conference on
Conference_Location :
Honolulu, HI
Print_ISBN :
978-1-4503-0445-0
Electronic_ISBN :
0270-5257
DOI :
10.1145/1985793.1986020
