Mining historical software testing outcomes to predict future results

Authors

  • Abdulshaheed M Department of Computer Science, University of Bahrain, Sakheer, Kingdom of Bahrain
  • Hammad M Department of Computer Science, University of Bahrain, Sakheer, Kingdom of Bahrain
  • Alqaddoumi A Department of Computer Science, University of Bahrain, Sakheer, Kingdom of Bahrain
  • Obeidat Q Department of Computer Science, University of Bahrain, Sakheer, Kingdom of Bahrain

Keywords:

software engineering, machine learning, prediction model, software defects, software evolution

Abstract

Software bugs and program defects have significant negative effect on the cost and duration of software development process. Finding such bugs in early stages of the development process will cuts development time and maintenance costs. This investigation presents three different machine learning algorithms: K-Nearest Neighbors (KNN), Random Forest (RF), and Multilayer Perceptron (MLP) to build a new proposed software defect prediction model using different types of software performance metrics. This proposed model was tested on three public datasets obtained from NASA to assess its accuracy and revealed that the KNN was outperforms RF and MLP.

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Published

2024-02-26

How to Cite

Abdulshaheed, M., Hammad, M., Alqaddoumi, A., & Obeidat, Q. (2024). Mining historical software testing outcomes to predict future results. COMPUSOFT: An International Journal of Advanced Computer Technology, 8(12), 3525–3529. Retrieved from https://ijact.in/index.php/j/article/view/549

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Section

Original Research Article

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