IDEAS home Printed from https://ideas.repec.org/a/spr/ijsaem/v9y2018i1d10.1007_s13198-016-0543-y.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13198-016-0543-y
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s13198-016-0543-y?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kwang Yoon Song & In Hong Chang & Hoang Pham, 2019. "A Testing Coverage Model Based on NHPP Software Reliability Considering the Software Operating Environment and the Sensitivity Analysis," Mathematics, MDPI, vol. 7(5), pages 1-21, May.
    2. Gaver, Donald P. & Jacobs, Patricia A., 2014. "Reliability growth by failure mode removal," Reliability Engineering and System Safety, Elsevier, vol. 130(C), pages 27-32.
    3. Pievatolo, Antonio & Ruggeri, Fabrizio & Soyer, Refik, 2012. "A Bayesian hidden Markov model for imperfect debugging," Reliability Engineering and System Safety, Elsevier, vol. 103(C), pages 11-21.
    4. Utkin, Lev V. & Coolen, Frank P.A., 2018. "A robust weighted SVR-based software reliability growth model," Reliability Engineering and System Safety, Elsevier, vol. 176(C), pages 93-101.
    5. Da Hye Lee & In Hong Chang & Hoang Pham, 2020. "Software Reliability Model with Dependent Failures and SPRT," Mathematics, MDPI, vol. 8(8), pages 1-14, August.
    6. Peng, R. & Li, Y.F. & Zhang, W.J. & Hu, Q.P., 2014. "Testing effort dependent software reliability model for imperfect debugging process considering both detection and correction," Reliability Engineering and System Safety, Elsevier, vol. 126(C), pages 37-43.
    7. Wei, Zhao & Tao, Tao & ZhuoShu, Ding & Zio, Enrico, 2013. "A dynamic particle filter-support vector regression method for reliability prediction," Reliability Engineering and System Safety, Elsevier, vol. 119(C), pages 109-116.
    8. Pham, Hoang, 2003. "Software reliability and cost models: Perspectives, comparison, and practice," European Journal of Operational Research, Elsevier, vol. 149(3), pages 475-489, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:ijsaem:v:9:y:2018:i:1:d:10.1007_s13198-016-0543-y. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.