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Graph-based machine learning improves just-in-time defect prediction.

Jonathan Bryan1, Pablo Moriano2

  • 1AT&T Cybersecurity, AT&T, Atlanta, GA, United States of America.

Plos One
|April 13, 2023
PubMed
Summary
This summary is machine-generated.

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This study introduces graph-based machine learning (ML) for Just-In-Time (JIT) defect prediction. Contribution graphs improve defect prediction accuracy, outperforming traditional ML methods.

Area of Science:

  • Software Engineering
  • Machine Learning
  • Software Quality Assurance

Background:

  • Modern software development involves complex collaboration among numerous developers, increasing the likelihood of defect-prone changes.
  • Traditional machine learning (ML) methods for predicting software defects have reached a performance plateau.
  • Accurately identifying defect-prone changes in complex software systems remains a significant challenge.

Purpose of the Study:

  • To explore the potential of graph-based ML for Just-In-Time (JIT) defect prediction.
  • To investigate whether features extracted from developer-source file contribution graphs outperform intrinsic software features in predicting defects.
  • To develop and evaluate a novel approach to JIT defect prediction using contribution graphs.

Main Methods:

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  • Constructed contribution graphs representing developers and source files to model software change complexity.
  • Applied graph-based ML techniques to classify edges (changes) as defect-prone or not.
  • Evaluated the proposed method on 14 open-source software projects.

Main Results:

  • Graph-based ML features significantly improved JIT defect prediction compared to traditional methods.
  • The best model achieved an F1 score of 77.55% and a Matthews correlation coefficient (MCC) of 53.16%.
  • This represents a 152% increase in F1 score and a 3% increase in MCC over state-of-the-art JIT defect prediction.

Conclusions:

  • Framing JIT defect prediction using contribution graphs and graph-based ML offers a more effective approach.
  • The proposed method demonstrates superior performance in identifying defect-prone code changes.
  • This research opens avenues for operationalizing advanced JIT defect prediction in software development.