An empirical study of Linespots: A novel past‐fault algorithm. (18th August 2021)

• Record Type: Journal Article
• Authors: Scholz, Maximilian; Torkar, Richard
• Is Part Of: Software testing, verification & reliability. Volume 31:Number 8(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-08-18
• Subjects: Bayesian data analysis -- Bugspots -- directed acyclic graphs -- fault prediction -- Linespots -- past faults; Computer software -- Testing -- Periodicals; Computer software -- Verification -- Periodicals; Computer software -- Reliability -- Periodicals; 005.14
• DOI: 10.1002/stvr.1787
• Languages: English
• ISSNs: 0960-0833

Abstract

Summary: This paper proposes the novel past‐faults fault prediction algorithm Linespots, based on the Bugspots algorithm. We analyse the predictive performance and runtime of Linespots compared with Bugspots with an empirical study using the most significant self‐built dataset as of now, including high‐quality samples for validation. As a novelty in fault prediction, we use Bayesian data analysis and Directed Acyclic Graphs to model the effects. We found consistent improvements in the predictive performance of Linespots over Bugspots for all seven evaluation metrics. We conclude that Linespots should be used over Bugspots in all cases where no real‐time performance is necessary. Abstract : We propose the novel past‐faults fault prediction algorithm Linespots, a line granularity version of Google's Bugspots algorithm. We use Bayesian data analysis to model the effects and found consistent improvements across a range of predictive performance metrics. We conclude that Linespots should be used over Bugspots in most cases.