Uncovering Behavioural Patterns of One: And Binary-Class SVM-Based Software Defect Predictors

George Ciubotariu, Gabriela Czibula, Istvan Czibula, Ioana-Gabriela Chelaru

2023

Abstract

Software defect prediction is a relevant task, that increasingly gains more interest as the programming industry expands. However, one of its difficulties consists in overcoming class imbalance issues, because most open-source software projects that are annotated using bug tracking systems do not have lots of defects. Therefore, the rarity of bugs may often cause machine learning models to dramatically underperform, even when diverse data augmentation or selection methods are applied. As a result, our focus shifts towards one-class classification, which is a family of outlier detection algorithms, designed to be trained on data instances of a single label. Considering this approach, we are adapting the traditional Support Vector Machine model to perform outlier detection. Experiments are performed on 16 versions of an open-source medium-sized software system, the Apache Calcite software. We are performing an extensive assessment of the ability of one-class classifiers trained on software defects to effectively discriminate between defective and non-defective software entities. The main findings of our study consist in uncovering several trends in the behaviour of the one- and binary-class support vector machine-based models when solving SDP problems.

Paper Citation

in Harvard Style

Ciubotariu G., Czibula G., Czibula I. and Chelaru I. (2023). Uncovering Behavioural Patterns of One: And Binary-Class SVM-Based Software Defect Predictors. In Proceedings of the 18th International Conference on Software Technologies - Volume 1: ICSOFT; ISBN 978-989-758-665-1, SciTePress, pages 249-257. DOI: 10.5220/0012052700003538

in Bibtex Style

@conference{icsoft23,
author={George Ciubotariu and Gabriela Czibula and Istvan Czibula and Ioana-Gabriela Chelaru},
title={Uncovering Behavioural Patterns of One: And Binary-Class SVM-Based Software Defect Predictors},
booktitle={Proceedings of the 18th International Conference on Software Technologies - Volume 1: ICSOFT},
year={2023},
pages={249-257},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012052700003538},
isbn={978-989-758-665-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Conference on Software Technologies - Volume 1: ICSOFT
TI - Uncovering Behavioural Patterns of One: And Binary-Class SVM-Based Software Defect Predictors
SN - 978-989-758-665-1
AU - Ciubotariu G.
AU - Czibula G.
AU - Czibula I.
AU - Chelaru I.
PY - 2023
SP - 249
EP - 257
DO - 10.5220/0012052700003538
PB - SciTePress