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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Différents modèles de prédiction de pannes logicielles ont été proposés au cours des vingt dernières années. De nombreuses études ont comparé et évalué les approches de prédiction existantes afin d’identifier les plus efficaces. Cependant, dans la plupart des cas, ces modèles et techniques fournissent des résultats variables, et leurs résultats ne conduisent pas à la meilleure performance possible sur différents ensembles de données. Cela est principalement dû à la nature diversifiée des projets de développement de logiciels et il existe donc un risque que les modèles sélectionnés conduisent à des résultats incohérents sur plusieurs ensembles de données. Dans ce travail, nous proposons l'utilisation d'algorithmes de bandit dans les cas où la précision des modèles est incohérente sur plusieurs ensembles de données. Dans l'expérience discutée dans ce travail, nous avons utilisé quatre modèles de prédiction conventionnels, testés sur trois ensembles de données différents, puis avons sélectionné dynamiquement le meilleur modèle possible en appliquant des algorithmes de bandit. Nous avons ensuite comparé nos résultats avec ceux obtenus par vote majoritaire. En conséquence, Epsilon-greedy avec ϵ = 0.3 a montré la meilleure ou la deuxième meilleure performance de prédiction par rapport à l'utilisation d'un seul modèle de prédiction et du vote majoritaire. Nos résultats ont montré que les algorithmes de bandit peuvent fournir des résultats prometteurs lorsqu’ils sont utilisés dans la prédiction des pannes.
Teruki HAYAKAWA
Kindai University
Masateru TSUNODA
Kindai University
Koji TODA
Fukuoka Institute of Technology
Keitaro NAKASAI
Nara Institute of Science and Technology
Amjed TAHIR
Massey University
Kwabena Ebo BENNIN
Wageningen University & Research
Akito MONDEN
Okayama University
Kenichi MATSUMOTO
Nara Institute of Science and Technology
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Teruki HAYAKAWA, Masateru TSUNODA, Koji TODA, Keitaro NAKASAI, Amjed TAHIR, Kwabena Ebo BENNIN, Akito MONDEN, Kenichi MATSUMOTO, "A Novel Approach to Address External Validity Issues in Fault Prediction Using Bandit Algorithms" in IEICE TRANSACTIONS on Information,
vol. E104-D, no. 2, pp. 327-331, February 2021, doi: 10.1587/transinf.2020EDL8098.
Abstract: Various software fault prediction models have been proposed in the past twenty years. Many studies have compared and evaluated existing prediction approaches in order to identify the most effective ones. However, in most cases, such models and techniques provide varying results, and their outcomes do not result in best possible performance across different datasets. This is mainly due to the diverse nature of software development projects, and therefore, there is a risk that the selected models lead to inconsistent results across multiple datasets. In this work, we propose the use of bandit algorithms in cases where the accuracy of the models are inconsistent across multiple datasets. In the experiment discussed in this work, we used four conventional prediction models, tested on three different dataset, and then selected the best possible model dynamically by applying bandit algorithms. We then compared our results with those obtained using majority voting. As a result, Epsilon-greedy with ϵ=0.3 showed the best or second-best prediction performance compared with using only one prediction model and majority voting. Our results showed that bandit algorithms can provide promising outcomes when used in fault prediction.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2020EDL8098/_p
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@ARTICLE{e104-d_2_327,
author={Teruki HAYAKAWA, Masateru TSUNODA, Koji TODA, Keitaro NAKASAI, Amjed TAHIR, Kwabena Ebo BENNIN, Akito MONDEN, Kenichi MATSUMOTO, },
journal={IEICE TRANSACTIONS on Information},
title={A Novel Approach to Address External Validity Issues in Fault Prediction Using Bandit Algorithms},
year={2021},
volume={E104-D},
number={2},
pages={327-331},
abstract={Various software fault prediction models have been proposed in the past twenty years. Many studies have compared and evaluated existing prediction approaches in order to identify the most effective ones. However, in most cases, such models and techniques provide varying results, and their outcomes do not result in best possible performance across different datasets. This is mainly due to the diverse nature of software development projects, and therefore, there is a risk that the selected models lead to inconsistent results across multiple datasets. In this work, we propose the use of bandit algorithms in cases where the accuracy of the models are inconsistent across multiple datasets. In the experiment discussed in this work, we used four conventional prediction models, tested on three different dataset, and then selected the best possible model dynamically by applying bandit algorithms. We then compared our results with those obtained using majority voting. As a result, Epsilon-greedy with ϵ=0.3 showed the best or second-best prediction performance compared with using only one prediction model and majority voting. Our results showed that bandit algorithms can provide promising outcomes when used in fault prediction.},
keywords={},
doi={10.1587/transinf.2020EDL8098},
ISSN={1745-1361},
month={February},}
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TY - JOUR
TI - A Novel Approach to Address External Validity Issues in Fault Prediction Using Bandit Algorithms
T2 - IEICE TRANSACTIONS on Information
SP - 327
EP - 331
AU - Teruki HAYAKAWA
AU - Masateru TSUNODA
AU - Koji TODA
AU - Keitaro NAKASAI
AU - Amjed TAHIR
AU - Kwabena Ebo BENNIN
AU - Akito MONDEN
AU - Kenichi MATSUMOTO
PY - 2021
DO - 10.1587/transinf.2020EDL8098
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E104-D
IS - 2
JA - IEICE TRANSACTIONS on Information
Y1 - February 2021
AB - Various software fault prediction models have been proposed in the past twenty years. Many studies have compared and evaluated existing prediction approaches in order to identify the most effective ones. However, in most cases, such models and techniques provide varying results, and their outcomes do not result in best possible performance across different datasets. This is mainly due to the diverse nature of software development projects, and therefore, there is a risk that the selected models lead to inconsistent results across multiple datasets. In this work, we propose the use of bandit algorithms in cases where the accuracy of the models are inconsistent across multiple datasets. In the experiment discussed in this work, we used four conventional prediction models, tested on three different dataset, and then selected the best possible model dynamically by applying bandit algorithms. We then compared our results with those obtained using majority voting. As a result, Epsilon-greedy with ϵ=0.3 showed the best or second-best prediction performance compared with using only one prediction model and majority voting. Our results showed that bandit algorithms can provide promising outcomes when used in fault prediction.
ER -