A novel approach to address external validity issues in fault prediction using bandit algorithms

Teruki HAYAKAWA, Masateru TSUNODA, Koji TODA, Keitaro NAKASAI, Amjed TAHIR, Kwabena Ebo BENNIN, Akito MONDEN, Kenichi MATSUMOTO

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Pages (from-to)327-331
Number of pages5
JournalIEICE Transactions on Information and Systems
VolumeE104D
Issue number2
DOIs
Publication statusPublished - Feb 1 2021

Keywords

  • Defect prediction
  • Diversity of datasets
  • Dynamic model selection
  • Multi-armed bandit
  • Risk-based testing

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Artificial Intelligence

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