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A trustworthy hybrid model for transparent software defect prediction: SPAM-XAI

Author

Listed:
  • Mohd Mustaqeem
  • Suhel Mustajab
  • Mahfooz Alam
  • Fathe Jeribi
  • Shadab Alam
  • Mohammed Shuaib

Abstract

Maintaining quality in software development projects is becoming very difficult because the complexity of modules in the software is growing exponentially. Software defects are the primary concern, and software defect prediction (SDP) plays a crucial role in detecting faulty modules early and planning effective testing to reduce maintenance costs. However, SDP faces challenges like imbalanced data, high-dimensional features, model overfitting, and outliers. Moreover, traditional SDP models lack transparency and interpretability, which impacts stakeholder confidence in the Software Development Life Cycle (SDLC). We propose SPAM-XAI, a hybrid model integrating novel sampling, feature selection, and eXplainable-AI (XAI) algorithms to address these challenges. The SPAM-XAI model reduces features, optimizes the model, and reduces time and space complexity, enhancing its robustness. The SPAM-XAI model exhibited improved performance after experimenting with the NASA PROMISE repository’s datasets. It achieved an accuracy of 98.13% on CM1, 96.00% on PC1, and 98.65% on PC2, surpassing previous state-of-the-art and baseline models with other evaluation matrices enhancement compared to existing methods. The SPAM-XAI model increases transparency and facilitates understanding of the interaction between features and error status, enabling coherent and comprehensible predictions. This enhancement optimizes the decision-making process and enhances the model’s trustworthiness in the SDLC.

Suggested Citation

  • Mohd Mustaqeem & Suhel Mustajab & Mahfooz Alam & Fathe Jeribi & Shadab Alam & Mohammed Shuaib, 2024. "A trustworthy hybrid model for transparent software defect prediction: SPAM-XAI," PLOS ONE, Public Library of Science, vol. 19(7), pages 1-33, July.
  • Handle: RePEc:plo:pone00:0307112
    DOI: 10.1371/journal.pone.0307112
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    References listed on IDEAS

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    1. Somya Goyal, 2022. "Effective software defect prediction using support vector machines (SVMs)," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(2), pages 681-696, April.
    2. Rashid Naseem & Bilal Khan & Arshad Ahmad & Ahmad Almogren & Saima Jabeen & Bashir Hayat & Muhammad Arif Shah, 2020. "Investigating Tree Family Machine Learning Techniques for a Predictive System to Unveil Software Defects," Complexity, Hindawi, vol. 2020, pages 1-21, November.
    3. Kun Song & ShengKai Lv & Die Hu & Peng He, 2021. "Software Defect Prediction Based on Elman Neural Network and Cuckoo Search Algorithm," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-14, November.
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