An empirical study of Linespots: A novel past-fault algorithm

TL;DR

This paper introduces Linespots, a new fault prediction algorithm based on Bugspots, which uses Bayesian analysis and DAGs to improve predictive accuracy over the original Bugspots method, validated through extensive empirical evaluation.

Contribution

The paper presents Linespots, a novel fault prediction algorithm that enhances Bugspots by incorporating Bayesian data analysis and DAGs, demonstrating superior performance.

Findings

Linespots outperforms Bugspots on all evaluation metrics.

Linespots shows consistent predictive improvements.

Recommended for non-real-time fault prediction scenarios.

Abstract

This paper proposes the novel past-faults fault prediction algorithm Linespots, based on the Bugspots algorithm. We analyze the predictive performance and runtime of Linespots compared to 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.