Using Bandit Algorithms for Selecting Feature Reduction Techniques in Software Defect Prediction

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

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

Abstract

Background: Selecting a suitable feature reduction technique, when building a defect prediction model, can be challenging. Different techniques can result in the selection of different independent variables which have an impact on the overall performance of the prediction model. To help in the selection, previous studies have assessed the impact of each feature reduction technique using different datasets. However, there are many reduction techniques, and therefore some of the well-known techniques have not been assessed by those studies. Aim: The goal of the study is to select a high-accuracy reduction technique from several candidates without preliminary assessments. Method: We utilized bandit algorithm (BA) to help with the selection of best features reduction technique for a list of candidates. To select the best feature reduction technique, BA evaluates the prediction accuracy of the candidates, comparing testing results of different modules with their prediction results. By substituting the reduction technique for the prediction method, BA can then be used to select the best reduction technique. In the experiment, we evaluated the performance of BA to select suitable reduction technique. We performed cross version defect prediction using 14 datasets. As feature reduction techniques, we used two assessed and two non-assessed techniques. Results: Using BA, the prediction accuracy was higher or equivalent than existing approaches on average, compared with techniques selected based on an assessment. Conclusions: BA can have larger impact on improving prediction models by helping not only on selecting suitable models, but also in selecting suitable feature reduction techniques.

Original languageEnglish
Title of host publicationProceedings - 2022 Mining Software Repositories Conference, MSR 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages670-681
Number of pages12
ISBN (Electronic)9781450393034
DOIs
Publication statusPublished - 2022
Event2022 Mining Software Repositories Conference, MSR 2022 - Pittsburgh, United States
Duration: May 23 2022May 24 2022

Publication series

NameProceedings - 2022 Mining Software Repositories Conference, MSR 2022

Conference

Conference2022 Mining Software Repositories Conference, MSR 2022
Country/TerritoryUnited States
CityPittsburgh
Period5/23/225/24/22

Keywords

  • external validity
  • online optimization
  • Software fault prediction
  • variable selection

ASJC Scopus subject areas

  • Software
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality

Fingerprint

Dive into the research topics of 'Using Bandit Algorithms for Selecting Feature Reduction Techniques in Software Defect Prediction'. Together they form a unique fingerprint.

Cite this