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An Empirical Study of Training Data Selection Methods for Ranking-Oriented Cross-Project Defect Prediction.

Haoyu Luo1, Heng Dai2, Weiqiang Peng3

  • 1School of Computer Science, South China Normal University, Guangzhou 510631, China.

Sensors (Basel, Switzerland)
|November 27, 2021
PubMed
Summary

This study investigated training data selection for ranking-oriented cross-project defect prediction (ROCPDP). ROCPDP models using filtered cross-project data outperform models using limited within-project data when no sufficient historical data is available.

Keywords:
data selectionfault predictionmachine learning

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Area of Science:

  • Software Engineering
  • Software Quality Assurance
  • Empirical Software Engineering

Background:

  • Ranking-oriented cross-project defect prediction (ROCPDP) aims to rank software modules by defect likelihood in new projects.
  • A key challenge in ROCPDP is the data distribution shift between source and target projects, impacting performance.
  • Investigating training data selection is crucial for improving ROCPDP model effectiveness.

Purpose of the Study:

  • To evaluate the impact of nine distinct training data selection methods on ROCPDP model performance.
  • To compare ROCPDP models trained on filtered cross-project data against ranking-oriented within-project defect prediction (ROWPDP) models.
  • To provide guidance on utilizing cross-project data for defect prediction when within-project data is scarce.

Main Methods:

  • Examined nine training data selection methods, including a global filter, for ROCPDP.
  • Compared ROCPDP models with ROWPDP models trained on sufficient and limited within-project data.
  • Utilized eleven industrial defect datasets and evaluated performance using FPA and Norm(Popt) metrics.

Main Results:

  • No statistically significant performance differences were found among the nine training data selection methods.
  • ROCPDP models trained on filtered cross-project data did not match the performance of ROWPDP models using sufficient within-project data.
  • ROCPDP models trained on filtered cross-project data showed superior performance compared to ROWPDP models using limited within-project data.

Conclusions:

  • The choice among the nine evaluated training data selection methods does not significantly impact ROCPDP performance.
  • When sufficient historical within-project data is unavailable, leveraging other project datasets for ROCPDP is a viable strategy.
  • Software quality teams should consider using cross-project data for ROCPDP in scenarios with limited or no within-project data.