Predicting the Number of Software Faults using Deep Learning

Authors

  • Wahaj Alkaberi Computer Science and Artificial Intelligence Department, College of Computer Science and Engineering, University of Jeddah, Saudi Arabia
  • Fatmah Assiri Software Engineering Department, College of Computer Science and Engineering, University of Jeddah, Saudi Arabia
Volume: 14 | Issue: 2 | Pages: 13222-13231 | April 2024 | https://doi.org/10.48084/etasr.6798

Abstract

The software testing phase requires considerable time, effort, and cost, particularly when there are many faults. Thus, developers focus on the evolution of Software Fault Prediction (SFP) to predict faulty units in advance, therefore, improving software quality significantly. Forecasting the number of faults in software units can efficiently direct software testing efforts. Previous studies have employed several machine learning models to determine whether a software unit is faulty. In this study, a new, simple deep neural network approach that can adapt to the type of input data was designed, utilizing Convolutional Neural Networks (CNNs) and Multi-Layer Perceptron (MLP), to predict the number of software faults. Twelve open-source software project datasets from the PROMISE repository were used for testing and validation. As data imbalance can negatively impact prediction accuracy, the new version of synthetic minority over-sampling technique (SMOTEND) was used to resolve data imbalance. In experimental results, a lower error rate was obtained for MLP, compared to CNN, reaching 0.195, indicating the accuracy of this prediction model. The proposed approach proved to be effective when compared with two of the best machine learning models in the field of prediction. The code will be available on GitHub.

Keywords:

deep learning, MLP, CNN, software testing, prediction, fault, class imbalance

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How to Cite

[1]
W. Alkaberi and F. Assiri, “Predicting the Number of Software Faults using Deep Learning”, Eng. Technol. Appl. Sci. Res., vol. 14, no. 2, pp. 13222–13231, Apr. 2024.

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