The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Pelbagai model ramalan kerosakan perisian telah dicadangkan dalam dua puluh tahun yang lalu. Banyak kajian telah membandingkan dan menilai pendekatan ramalan sedia ada untuk mengenal pasti pendekatan yang paling berkesan. Walau bagaimanapun, dalam kebanyakan kes, model dan teknik sedemikian memberikan hasil yang berbeza-beza, dan hasil mereka tidak menghasilkan prestasi terbaik merentas set data yang berbeza. Ini disebabkan terutamanya oleh kepelbagaian sifat projek pembangunan perisian, dan oleh itu, terdapat risiko bahawa model yang dipilih membawa kepada hasil yang tidak konsisten merentas berbilang set data. Dalam kerja ini, kami mencadangkan penggunaan algoritma penyamun dalam kes di mana ketepatan model tidak konsisten merentas berbilang set data. Dalam eksperimen yang dibincangkan dalam kerja ini, kami menggunakan empat model ramalan konvensional, diuji pada tiga set data berbeza, dan kemudian memilih model terbaik yang mungkin secara dinamik dengan menggunakan algoritma penyamun. Kami kemudian membandingkan keputusan kami dengan yang diperoleh menggunakan undian majoriti. Hasilnya, Epsilon-greedy dengan ϵ=0.3 menunjukkan prestasi ramalan terbaik atau kedua terbaik berbanding dengan hanya menggunakan satu model ramalan dan undian majoriti. Keputusan kami menunjukkan bahawa algoritma penyamun boleh memberikan hasil yang menjanjikan apabila digunakan dalam ramalan kesalahan.
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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Salinan
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
Salinan
@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},}
Salinan
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 -