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Software defect prediction using learning to rank approach.

Ali Bou Nassif1, Manar Abu Talib2, Mohammad Azzeh3

  • 1Department of Computer Engineering, University of Sharjah, Sharjah, United Arab Emirates. anassif@sharjah.ac.ae.

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This summary is machine-generated.

Learning to Rank (LTR) effectively predicts and ranks software defects. Bug count yields more stable results than bug density, while feature selection and imbalance learning offer no significant improvements for LTR in software defect prediction.

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Area of Science:

  • Software Engineering
  • Machine Learning
  • Data Science

Background:

  • Software defect prediction (SDP) is crucial for optimizing resource allocation and minimizing testing costs.
  • Project managers need to rank defective modules, not just identify them, especially within budget constraints.
  • Learning to Rank (LTR) is a machine learning methodology applicable to SDP for predicting and ranking defective modules.

Purpose of the Study:

  • To conduct a comprehensive comparison of eight selected LTR models for SDP.
  • To evaluate the impact of bug count versus bug density as target variables.
  • To assess the effect of imbalance learning and feature selection on LTR model performance in SDP.

Main Methods:

  • Empirical evaluation of eight LTR models using Fault Percentile Average.
  • Comparison of LTR models with two target variables: bug count and bug density.
  • Analysis of the influence of imbalance learning and feature selection techniques.

Main Results:

  • Bug count as a ranking criterion produced higher scores and more stable results compared to bug density.
  • Imbalance learning positively impacted bug density prediction but negatively affected bug count prediction.
  • Feature selection showed no significant improvement for bug density and no impact for bug count.

Conclusions:

  • Using bug count as the ranking criterion in LTR for SDP is more effective and stable.
  • Imbalance learning and feature selection do not consistently enhance LTR performance in SDP and may not yield superior results.