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.
%0 Journal Article
%1 https://doi.org/10.1002/stvr.1787
%A Scholz, Maximilian
%A Torkar, Richard
%D 2021
%J Software Testing, Verification and Reliability
%K EXC2075 graduateSchool peerReviewed pn6
%N n/a
%P e1787
%R https://doi.org/10.1002/stvr.1787
%T An empirical study of Linespots: A novel past-fault algorithm
%U https://onlinelibrary.wiley.com/doi/abs/10.1002/stvr.1787
%V n/a
%X 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.
@article{https://doi.org/10.1002/stvr.1787,
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.},
added-at = {2021-09-23T15:15:44.000+0200},
author = {Scholz, Maximilian and Torkar, Richard},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/23dc0dcf4fc7ea3821692eff620ec0b2b/jonasnicodemus},
doi = {https://doi.org/10.1002/stvr.1787},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/stvr.1787},
interhash = {fbc6514ea574757fe815f5d3b5e69c19},
intrahash = {3dc0dcf4fc7ea3821692eff620ec0b2b},
journal = {Software Testing, Verification and Reliability},
keywords = {EXC2075 graduateSchool peerReviewed pn6},
number = {n/a},
pages = {e1787},
timestamp = {2021-09-23T13:17:38.000+0200},
title = {An empirical study of Linespots: A novel past-fault algorithm},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/stvr.1787},
volume = {n/a},
year = 2021
}
