University of Oulu

F. Lomio, L. Pascarella, F. Palomba and V. Lenarduzzi, "Regularity or Anomaly? On The Use of Anomaly Detection for Fine-Grained JIT Defect Prediction," 2022 48th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), Gran Canaria, Spain, 2022, pp. 270-273, doi: 10.1109/SEAA56994.2022.00049

Regularity or anomaly? : on the use of anomaly detection for fine-grained JIT defect prediction

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Author: Lomio, Francesco1; Pascarella, Luca2; Palomba, Fabio3;
Organizations: 1Tampere University
2Università della Svizzera Italiana
3University of Salerno
4University of Oulu
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.2 MB)
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Language: English
Published: IEEE, 2022
Publish Date: 2023-03-23


Fine-grained just-in-time defect prediction aims at identifying likely defective files within new commits. Popular techniques are based on supervised learning, where machine learning algorithms are fed with historical data. One of the limitations of these techniques is concerned with the use of imbalanced data that only contain a few defective samples to enable a proper learning phase. To overcome this problem, recent work has shown that anomaly detection can be used as an alternative. With our study, we aim at assessing how anomaly detection can be employed for the problem of fine-grained just-in-time defect prediction. We conduct an empirical investigation on 32 open-source projects, designing and evaluating three anomaly detection methods for fine-grained just-in-time defect prediction. Our results do not show significant advantages that justify the benefit of anomaly detection over machine learning approaches.

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ISBN: 978-1-6654-6152-8
ISBN Print: 978-1-6654-6153-5
Pages: 270 - 273
DOI: 10.1109/SEAA56994.2022.00049
Host publication: 2022 48th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)
Conference: Euromicro Conference on Software Engineering and Advanced Applications
Type of Publication: A4 Article in conference proceedings
Field of Science: 113 Computer and information sciences
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