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Authors: Pasquale Ardimento 1 ; Lerina Aversano 2 ; Mario 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

ISBN: 978-989-758-443-5

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.

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Paper citation in several formats:
Ardimento, P.; Aversano, L.; Bernardi, Mario Luca and Cimitile, M. (2020). Temporal Convolutional Networks for Just-in-Time Software Defect Prediction.In Proceedings of the 15th International Conference on Software Technologies - Volume 1: ICSOFT, ISBN 978-989-758-443-5, pages 384-393. DOI: 10.5220/0009890003840393

@conference{icsoft20,
author={Pasquale Ardimento. and Lerina Aversano. and Bernardi, Mario Luca and Marta Cimitile.},
title={Temporal Convolutional Networks for Just-in-Time Software Defect Prediction},
booktitle={Proceedings of the 15th International Conference on Software Technologies - Volume 1: ICSOFT,},
year={2020},
pages={384-393},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009890003840393},
isbn={978-989-758-443-5},
}

TY - CONF

JO - Proceedings of the 15th International Conference on Software Technologies - Volume 1: ICSOFT,
TI - Temporal Convolutional Networks for Just-in-Time Software Defect Prediction
SN - 978-989-758-443-5
AU - Ardimento, P.
AU - Aversano, L.
AU - Bernardi, Mario Luca
AU - Cimitile, M.
PY - 2020
SP - 384
EP - 393
DO - 10.5220/0009890003840393

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