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Multilabel classification for defect prediction in software engineering.

Jalaj Pachouly1, Swati Ahirrao2, Ketan Kotecha3,4

  • 1Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, 412115, India.

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|March 14, 2025
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Summary
This summary is machine-generated.

This study introduces multilabel classification for software defect prediction, outperforming traditional methods. Balancing the dataset significantly improved machine learning and deep learning model performance for defect reports.

Keywords:
Class imbalanceData preprocessingDefect predictionFeature selectionMultilabel classificationSoftware engineering

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Area of Science:

  • Software Engineering
  • Artificial Intelligence
  • Machine Learning

Background:

  • Defect prediction is crucial in software development.
  • Traditional defect prediction uses multiclass classification, which is insufficient as defects can have multiple labels.
  • Multilabel classification offers a more appropriate approach for defect prediction.

Purpose of the Study:

  • To investigate the multilabel nature of software defects.
  • To apply machine learning and deep learning techniques for multilabel defect classification.
  • To address class imbalance and label correlations in defect prediction.

Main Methods:

  • Data wrangling of defect reports (title, body, comments, code snippets) into holistic summaries.
  • Implementation of traditional classifiers: Multinomial Naive Bayes, Logistic Regression, Random Forest.
  • Application of deep learning models: Multilayer Perceptron (MLP) and Convolutional Neural Network (CNN) with Classifier Chains.
  • Feature selection and dimensionality reduction using the chi-square test.
  • Handling class imbalance with Non-Negative Least Squares (NNLS).

Main Results:

  • Significant performance improvements observed in both machine learning and deep learning models after dataset balancing.
  • Effectiveness of multilabel classification demonstrated through evaluation metrics like Hamming loss, Recall, Precision, and F1-score.
  • Chi-square test proved useful for dataset quality assessment and feature selection.

Conclusions:

  • Multilabel classification is a more suitable approach for software defect prediction.
  • Dataset balancing is critical for enhancing the performance of defect prediction models.
  • The proposed methods provide a robust framework for improving defect prediction accuracy.