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Software reliability prediction using machine learning techniques

Author

Listed:
  • Arunima Jaiswal

    (Amity University)

  • Ruchika Malhotra

    (Delhi Technological University)

Abstract

Software Reliability is indispensable part of software quality and is one amongst the most inevitable aspect for evaluating quality of a software product. Software industry endures various challenges in developing highly reliable software. Application of machine learning (ML) techniques for software reliability prediction has shown meticulous and remarkable results. In this paper, we propose the use of ML techniques for software reliability prediction and evaluate them based on selected performance criteria. We have applied ML techniques including adaptive neuro fuzzy inference system (ANFIS), feed forward back propagation neural network, general regression neural network, support vector machines, multilayer perceptron, Bagging, cascading forward back propagation neural network, instance based learning, linear regression, M5P, reduced error pruning tree, M5Rules to predict the software reliability on various datasets being chosen from industrial software. Based on the experiments conducted, it was observed that ANFIS yields better results in all the cases and thus can be used for predicting software reliability since it predicts the reliability more accurately and precisely as compared to all other above mentioned techniques. In this study, we also made comparative analysis between cumulative failure data and inter failure time’s data and found that cumulative failure data gives better and more promising results as compared to inter failure time’s data.

Suggested Citation

  • Arunima Jaiswal & Ruchika Malhotra, 2018. "Software reliability prediction using machine learning techniques," 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. 9(1), pages 230-244, February.
    • Handle: RePEc:spr:ijsaem:v:9:y:2018:i:1:d:10.1007_s13198-016-0543-y
    DOI: 10.1007/s13198-016-0543-y
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    References listed on IDEAS

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    1. Yang, Bo & Li, Xiang & Xie, Min & Tan, Feng, 2010. "A generic data-driven software reliability model with model mining technique," Reliability Engineering and System Safety, Elsevier, vol. 95(6), pages 671-678.
    2. Xuemei Zhang & Daniel R. Jeske & Hoang Pham, 2002. "Calibrating software reliability models when the test environment does not match the user environment," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 18(1), pages 87-99, January.
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    Cited by:

    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. Yogita Khatri & Sandeep Kumar Singh, 2023. "An effective feature selection based cross-project defect prediction model for software quality improvement," 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. 14(1), pages 154-172, March.
    3. Ajit Kumar Behera & Mrutyunjaya Panda & Satchidananda Dehuri, 2021. "Software reliability prediction by recurrent artificial chemical link network," 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. 12(6), pages 1308-1321, December.

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