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Software Reliability Prediction Using Machine Learning Techniques

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Proceedings of Fifth International Conference on Soft Computing for Problem Solving

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 436))

Abstract

Software reliability is an indispensable part of software quality. 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 machine learning 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 backpropagation neural network (FFBPNN), general regression neural network (GRNN), support vector machines (SVM), multilayer perceptron (MLP), bagging, cascading forward backpropagation neural network (CFBPNN), instance-based learning (IBK), linear regression (Lin Reg), M5P, reduced error pruning tree (reptree), and 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 and it predicts the reliability more accurately and precisely as compared to all the above-mentioned techniques. In this study, we also made comparative analysis between cumulative failure data and inter failure time data and found that cumulative failure data give better and more promising results as compared to inter failure time data.

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Authors and Affiliations

  1. Delhi Technological University, Shahbad Daulatpur, Main Bawana Road, Delhi, 110042, India

    Arunima Jaiswal &  Ruchika Malhotra

Authors
  1. Arunima Jaiswal
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  2. Ruchika Malhotra
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Corresponding author

Correspondence to Arunima Jaiswal .

Editor information

Editors and Affiliations

  1. Dept of Applied Sci & Eng, Indian Instit of Tech Roorkee, Roorkee, India

    Millie Pant

  2. Department of Mathematics, Indian Inst of Tech Roorkee, Roorkee, India

    Kusum Deep

  3. Chankyapuri, Rm 327, South Asian Univ, Akbar Bhawan, New Delhi, India

    Jagdish Chand Bansal

  4. Department of Mathematics and Comp Sci, Liverpool Hope University, LIVERPOOL, United Kingdom

    Atulya Nagar

  5. Department of Mathematics, National Inst of Tech Silchar, Silchar, Assam, India

    Kedar Nath Das

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© 2016 Springer Science+Business Media Singapore

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Arunima Jaiswal, Ruchika Malhotra (2016). Software Reliability Prediction Using Machine Learning Techniques. In: Pant, M., Deep, K., Bansal, J., Nagar, A., Das, K. (eds) Proceedings of Fifth International Conference on Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 436. Springer, Singapore. https://doi.org/10.1007/978-981-10-0448-3_12

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  • DOI: https://doi.org/10.1007/978-981-10-0448-3_12

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-0447-6

  • Online ISBN: 978-981-10-0448-3

  • eBook Packages: EngineeringEngineering (R0)

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