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Improving effort-aware just-in-time defect prediction with weighted code churn and multi-objective slime mold

Hongming Dai1,2, Jianqing Xi1, Hong-Liang Dai3

  • 1School of Software, South China University of Technology, Guangzhou, 510006, China.

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Summary
This summary is machine-generated.

This study introduces a new method for effort-aware just-in-time software defect prediction (JIT-SDP) using weighted code churn and an improved slime mold algorithm. The WCMS method enhances software quality assurance (SQA) efficiency by better predicting defects.

Keywords:
Effort awareJust-in-time defect predictionMulti-objective slime mold algorithmSoftware quality assuranceWeighted code churn

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Area of Science:

  • Software Engineering
  • Computer Science
  • Artificial Intelligence

Background:

  • Effort-aware just-in-time software defect prediction (JIT-SDP) aims to optimize resource allocation in software quality assurance (SQA).
  • Existing JIT-SDP methods face challenges in accurately representing SQA effort and integrating advanced optimization algorithms.
  • These limitations lead to a performance gap between current JIT-SDP techniques and industry demands.

Purpose of the Study:

  • To propose a novel approach, weighted code churn (CC) and improved multi-objective slime mold algorithm (WCMS), for effort-aware JIT-SDP.
  • To address deficiencies in feature representation and model optimization within current JIT-SDP frameworks.
  • To enhance the efficiency and accuracy of software defect prediction.

Main Methods:

  • Developed a two-stage approach: Feature Improvement (FI) and Multi-Objective optimization (MO).
  • In FI, normalized Number of Modified Files (NF) and Entropy, quantifying change difficulty (DD = NF^Entropy) and defining weighted CC as change effort.
  • Enhanced the Slime Mold Algorithm (SMA) for multi-objective handling, synchronization, and conflict resolution, applying it to hyperparameter optimization (HPO) for JIT-SDP models.

Main Results:

  • Experimental evaluation on six open-source projects using effort-aware metrics demonstrated superior performance compared to benchmark methods.
  • The WCMS method showed significant improvements across cross-validation, time-wise cross-validation, and across-project prediction scenarios.
  • The proposed approach exhibited enhanced scalability and generalization capabilities.

Conclusions:

  • The WCMS method offers a more effective solution for effort-aware JIT-SDP by improving feature representation and optimization.
  • This advancement can lead to more efficient SQA practices and higher software quality.
  • The study highlights the potential of integrating advanced metaheuristic algorithms for complex software engineering tasks.