Using Bandit Algorithms for Project Selection in Cross-Project Defect Prediction

Takuya Asano, Masateru Tsunoda, Koji Toda, Amjed Tahir, Kwabena Ebo Bennin, Keitaro Nakasai, Akito Monden, Kenichi Matsumoto

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Background: defect prediction model is built using historical data from previous versions/releases of the same project. However, such historical data may not exist in case of newly developed projects. Alternatively, one can train a model using data obtained from external projects. This approach is known as cross-project defect prediction (CPDP). In CPDP, it is still difficult to utilize external projects' data or decide which particular project to use to train a model. Aim: to address this issue, we apply bandit algorithm (BA) to CPDP in order to select the most suitable training project from a set of projects. Method: BA-based prediction iteratively reselects the project after each module is tested, considering the accuracy of the predictions. As baselines, we used simple CPDP methods such as training a model with randomly selected project. All models were built using logistic regression. Results: We experimented our approach on two datasets (NASA and DAMB, with a total of 12 projects). The BA-based defect prediction models resulted in, on average, a higher accuracy (AUC and F1 score) than the baselines. Conclusion: in this preliminarily study, we demonstrate the feasibility of using BA in the context of CPDP. Our initial assessment shows that the use BA for predicting defects in CPDP is promising and may outperform existing approaches.

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE International Conference on Software Maintenance and Evolution, ICSME 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages649-653
Number of pages5
ISBN (Electronic)9781665428828
DOIs
Publication statusPublished - 2021
Event37th IEEE International Conference on Software Maintenance and Evolution, ICSME 2021 - Luxembourg City, Luxembourg
Duration: Sep 27 2021Oct 1 2021

Publication series

NameProceedings - 2021 IEEE International Conference on Software Maintenance and Evolution, ICSME 2021

Conference

Conference37th IEEE International Conference on Software Maintenance and Evolution, ICSME 2021
Country/TerritoryLuxembourg
CityLuxembourg City
Period9/27/2110/1/21

Keywords

  • CPFP
  • external validity
  • fault prediction
  • multi-armed bandit
  • online optimization
  • risk-based testing

ASJC Scopus subject areas

  • Software
  • Safety, Risk, Reliability and Quality

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