Authors:
Pasquale Ardimento
1
;
Lerina Aversano
2
;
Mario Luca Bernardi
3
and
Marta Cimitile
4
Affiliations:
1
Computer Science Department, University of Bari, Via E.Orabona 4, Bari, Italy
;
2
University of Sannio, Benevento, Italy
;
3
Department of Computing, Giustino Fortunato University, Benevento, Italy
;
4
Unitelma Sapienza, University of Rome, Italy
Keyword(s):
Machine Learning, Fault Prediction, Software Metrics.
Abstract:
Defect prediction and estimation techniques play a significant role in software maintenance and evolution. Recently, several research studies proposed just-in-time techniques to predict defective changes. Such prediction models make the developers check and fix the defects just at the time they are introduced (commit level). Nevertheless, early prediction of defects is still a challenging task that needs to be addressed and can be improved by getting higher performances. To address this issue this paper proposes an approach exploiting a large set of features corresponding to source code metrics detected from commits history of software projects. In particular, the approach uses deep temporal convolutional networks to make the fault prediction. The evaluation is performed on a large data-set, concerning four well-known open-source projects and shows that, under certain considerations, the proposed approach has effective defect proneness prediction ability.