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Study of the nonlinear imperfect software debugging model

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  • Wang, Jinyong
  • Wu, Zhibo

Abstract

In recent years there has been a dramatic proliferation of research on imperfect software debugging phenomena. Software debugging is a complex process and is affected by a variety of factors, including the environment, resources, personnel skills, and personnel psychologies. Therefore, the simple assumption that debugging is perfect is inconsistent with the actual software debugging process, wherein a new fault can be introduced when removing a fault. Furthermore, the fault introduction process is nonlinear, and the cumulative number of nonlinearly introduced faults increases over time. Thus, this paper proposes a nonlinear, NHPP imperfect software debugging model in consideration of the fact that fault introduction is a nonlinear process. The fitting and predictive power of the NHPP-based proposed model are validated through related experiments. Experimental results show that this model displays better fitting and predicting performance than the traditional NHPP-based perfect and imperfect software debugging models. S-confidence bounds are set to analyze the performance of the proposed model. This study also examines and discusses optimal software release-time policy comprehensively. In addition, this research on the nonlinear process of fault introduction is significant given the recent surge of studies on software-intensive products, such as cloud computing and big data.

Suggested Citation

  • Wang, Jinyong & Wu, Zhibo, 2016. "Study of the nonlinear imperfect software debugging model," Reliability Engineering and System Safety, Elsevier, vol. 153(C), pages 180-192.
    • Handle: RePEc:eee:reensy:v:153:y:2016:i:c:p:180-192
    DOI: 10.1016/j.ress.2016.05.003
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    References listed on IDEAS

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    1. 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.
    2. P. K. Kapur & H. Pham & A. Gupta & P. C. Jha, 2011. "Software Reliability Growth Models," Springer Series in Reliability Engineering, in: Software Reliability Assessment with OR Applications, chapter 0, pages 49-95, Springer.
    3. 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.
    4. Hoang Pham, 2006. "System Software Reliability," Springer Series in Reliability Engineering, Springer, number 978-1-84628-295-9, December.
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    Cited by:

    1. Qing Tian & Chun-Wu Yeh & Chih-Chiang Fang, 2022. "Bayesian Decision Making of an Imperfect Debugging Software Reliability Growth Model with Consideration of Debuggers’ Learning and Negligence Factors," Mathematics, MDPI, vol. 10(10), pages 1-21, May.
    2. Shakshi Singhal & P. K. Kapur & Vivek Kumar & Saurabh Panwar, 2024. "Stochastic debugging based reliability growth models for Open Source Software project," Annals of Operations Research, Springer, vol. 340(1), pages 531-569, September.
    3. Tahere Yaghoobi & Man-Fai Leung, 2023. "Modeling Software Reliability with Learning and Fatigue," Mathematics, MDPI, vol. 11(16), pages 1-20, August.
    4. Qing Tian & Chih-Chiang Fang & Chun-Wu Yeh, 2022. "Software Release Assessment under Multiple Alternatives with Consideration of Debuggers’ Learning Rate and Imperfect Debugging Environment," Mathematics, MDPI, vol. 10(10), pages 1-24, May.
    5. Wang, Jinyong & Zhang, Ce, 2018. "Software reliability prediction using a deep learning model based on the RNN encoder–decoder," Reliability Engineering and System Safety, Elsevier, vol. 170(C), pages 73-82.

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