Prediction of Software Defects Using Automated Machine Learning

Kazuya Tanaka, Akito Monden, Zeynep Yucel

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

Abstract

The effectiveness of defect prediction depends on modeling techniques as well as their parameter optimization, data preprocessing and ensemble development. This paper focuses on auto-sklearn, which is a recently-developed software library for automated machine learning, that can automatically select appropriate prediction models, hyperparameters and data preprocessing techniques for a given data set and develop their ensemble with optimized weights. In this paper we empirically evaluate the effectiveness of auto-sklearn in predicting the number of defects in software modules. In the experiment, we used software metrics of 20 OSS projects for cross-release defect prediction and compared auto-sklearn with random forest, decision tree and linear discriminant analysis by using Norm(Popt) as a performance measure. As a result, auto-sklearn showed similar prediction performance as random forest, which is one of the best prediction models for defect prediction in past studies. This indicates that auto-sklearn can obtain good prediction performance for defect prediction without any knowledge of machine learning techniques and models.

Original languageEnglish
Title of host publicationProceedings - 20th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2019
EditorsMasahide Nakamura, Hiroaki Hirata, Takayuki Ito, Takanobu Otsuka, Shun Okuhara
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages490-494
Number of pages5
ISBN (Electronic)9781728116518
DOIs
Publication statusPublished - Jul 2019
Event20th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2019 - Toyama, Japan
Duration: Jul 8 2019Jul 11 2019

Publication series

NameProceedings - 20th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2019

Conference

Conference20th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2019
CountryJapan
CityToyama
Period7/8/197/11/19

Keywords

  • auto-sklearn
  • cross-release prediction
  • defect prediction
  • meta-learning
  • software quality

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Hardware and Architecture
  • Software
  • Information Systems and Management

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  • Cite this

    Tanaka, K., Monden, A., & Yucel, Z. (2019). Prediction of Software Defects Using Automated Machine Learning. In M. Nakamura, H. Hirata, T. Ito, T. Otsuka, & S. Okuhara (Eds.), Proceedings - 20th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2019 (pp. 490-494). [8935839] (Proceedings - 20th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SNPD.2019.8935839