IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i10p1744-d819432.html
   My bibliography  Save this article

Software Release Assessment under Multiple Alternatives with Consideration of Debuggers’ Learning Rate and Imperfect Debugging Environment

Author

Listed:
  • Qing Tian

    (School of Computer Science and Software, Zhaoqing University, Zhaoqing 526061, China)

  • Chih-Chiang Fang

    (School of Computer Science and Software, Zhaoqing University, Zhaoqing 526061, China)

  • Chun-Wu Yeh

    (Computer and Game Development Program & Department of Information Management, Kun Shan University, Tainan 710303, Taiwan)

Abstract

In the software development life cycle, the quality and reliability of software are critical to software developers. Poor quality and reliability not only cause the loss of customers and sales but also increase the operational risk due to unreliable codes. Therefore, software developers should try their best to reduce such potential software defects by undertaking a software testing project. However, to pursue perfect and faultless software is unrealistic since the budget, time, and testing resources are limited, and the software developers need to reach a compromise that balances software reliability and the testing cost. Using the model presented in this study, software developers can devise multiple alternatives for a software testing project, and each alternative has its distinct allocation of human resources. The best alternative can therefore be selected. Furthermore, the allocation incorporates debuggers’ learning and negligent factors, both of which influence the efficiency of software testing in practice. Accordingly, the study considers both human factors and the nature of errors during the debugging process to develop a software reliability growth model to estimate the related costs and the reliability indicator. Additionally, the issue of error classification is also extended by considering the impacts of errors on the system, and the expected time required to remove simple or complex errors can be estimated based on different truncated exponential distributions. Finally, numerical examples are presented and sensitivity analyses are performed to provide managerial insights and useful directions to inform software release strategies.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:10:p:1744-:d:819432
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/10/1744/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/10/1744/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Aktekin, Tevfik & Caglar, Toros, 2013. "Imperfect debugging in software reliability: A Bayesian approach," European Journal of Operational Research, Elsevier, vol. 227(1), pages 112-121.
    2. 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.
    3. Lujia Wang & Qingpei Hu & Jian Liu, 2016. "Software reliability growth modeling and analysis with dual fault detection and correction processes," IISE Transactions, Taylor & Francis Journals, vol. 48(4), pages 359-370, April.
    4. 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.
    5. Chih-Chiang Fang & Chun-Wu Yeh, 2016. "Effective confidence interval estimation of fault-detection process of software reliability growth models," International Journal of Systems Science, Taylor & Francis Journals, vol. 47(12), pages 2878-2892, September.
    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. Mohammad Ubaidullah Bokhari & Md. Ashraf Siddiqui & Afaq Ahmad, 2021. "Integration of Testing Effort Function into Delayed S-Shaped Software Reliability Growth Model with Imperfect Debugging — a Proposed Bokhari Model," SN Operations Research Forum, Springer, vol. 2(4), pages 1-23, December.
    8. Pham, Hoang & Zhang, Xuemei, 2003. "NHPP software reliability and cost models with testing coverage," European Journal of Operational Research, Elsevier, vol. 145(2), pages 443-454, March.
    9. Zhao, Xingyu & Littlewood, Bev & Povyakalo, Andrey & Strigini, Lorenzo & Wright, David, 2018. "Conservative claims for the probability of perfection of a software-based system using operational experience of previous similar systems," Reliability Engineering and System Safety, Elsevier, vol. 175(C), pages 265-282.
    10. H. R. Marasi & M. Sedighi & H. Aydi & Y. U. Gaba & Ram Jiwari, 2021. "A Reliable Treatment for Nonlinear Differential Equations," Journal of Mathematics, Hindawi, vol. 2021, pages 1-5, December.
    11. Norah N. Al-Mutairi & Lutfiah I. Al-Turk & Sharifah A. Al-Rajhi, 2020. "A New Reliability Model Based on Lindley Distribution with Application to Failure Data," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-11, November.
    12. Chiu, Kuei-Chen & Huang, Yeu-Shiang & Lee, Tzai-Zang, 2008. "A study of software reliability growth from the perspective of learning effects," Reliability Engineering and System Safety, Elsevier, vol. 93(10), pages 1410-1421.
    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. Gabriela Czibula & Mihaiela Lupea & Anamaria Briciu, 2022. "Enhancing the Performance of Software Authorship Attribution Using an Ensemble of Deep Autoencoders," Mathematics, MDPI, vol. 10(15), pages 1-27, July.

    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. 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. Chih-Chiang Fang & Chun-Wu Yeh, 2016. "Effective confidence interval estimation of fault-detection process of software reliability growth models," International Journal of Systems Science, Taylor & Francis Journals, vol. 47(12), pages 2878-2892, September.
    3. Tabassum Naz Sindhu & Sadia Anwar & Marwa K. H. Hassan & Showkat Ahmad Lone & Tahani A. Abushal & Anum Shafiq, 2023. "An Analysis of the New Reliability Model Based on Bathtub-Shaped Failure Rate Distribution with Application to Failure Data," Mathematics, MDPI, vol. 11(4), pages 1-18, February.
    4. Chetna Choudhary & P. K. Kapur & Sunil K. Khatri & R. Muthukumar & Avinash K. Shrivastava, 2020. "Effort based release time of software for detection and correction processes using MAUT," 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. 11(2), pages 367-378, July.
    5. Tahere Yaghoobi & Man-Fai Leung, 2023. "Modeling Software Reliability with Learning and Fatigue," Mathematics, MDPI, vol. 11(16), pages 1-20, August.
    6. Landon, Joshua & Özekici, Süleyman & Soyer, Refik, 2013. "A Markov modulated Poisson model for software reliability," European Journal of Operational Research, Elsevier, vol. 229(2), pages 404-410.
    7. Byun, Ji-Eun & Noh, Hee-Min & Song, Junho, 2017. "Reliability growth analysis of k-out-of-N systems using matrix-based system reliability method," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 410-421.
    8. Aleksandr Kulikov & Pavel Ilyushin & Anton Loskutov & Konstantin Suslov & Sergey Filippov, 2022. "WSPRT Methods for Improving Power System Automation Devices in the Conditions of Distributed Generation Sources Operation," Energies, MDPI, vol. 15(22), pages 1-20, November.
    9. 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.
    10. Jørgen Vitting Andersen & Roy Cerqueti & Giulia Rotundo, 2017. "Rational expectations and stochastic systems," Documents de travail du Centre d'Economie de la Sorbonne 17060, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne, revised Oct 2019.
    11. Hiroyuki Okamura & Tadashi Dohi, 2016. "Phase-type software reliability model: parameter estimation algorithms with grouped data," Annals of Operations Research, Springer, vol. 244(1), pages 177-208, September.
    12. Adarsh Anand & Mohini Agarwal & Gunjan Bansal & A. H. S. Garmabaki, 2016. "Studying product diffusion based on market coverage," Journal of Marketing Analytics, Palgrave Macmillan, vol. 4(4), pages 135-146, December.
    13. 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.
    14. Subhashis Chatterjee & Ankur Shukla, 2016. "Change point–based software reliability model under imperfect debugging with revised concept of fault dependency," Journal of Risk and Reliability, , vol. 230(6), pages 579-597, December.
    15. Hirose, Hideo, 2012. "Estimation of the number of failures in the Weibull model using the ordinary differential equation," European Journal of Operational Research, Elsevier, vol. 223(3), pages 722-731.
    16. 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.
    17. Qian, Yanjun & Xie, Min & Goh, Thong Ngee & Lin, Jun, 2010. "Optimal testing strategies in overlapped design process," European Journal of Operational Research, Elsevier, vol. 206(1), pages 131-143, October.
    18. Ritu Bibyan & Sameer Anand & Anu G. Aggarwal & Abhishek Tandon, 2023. "Multi-release testing coverage-based SRGM considering error generation and change-point incorporating the random effect," 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(5), pages 1877-1887, October.
    19. 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.
    20. Littlewood, Bev & Salako, Kizito & Strigini, Lorenzo & Zhao, Xingyu, 2020. "On reliability assessment when a software-based system is replaced by a thought-to-be-better one," Reliability Engineering and System Safety, Elsevier, vol. 197(C).

    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:gam:jmathe:v:10:y:2022:i:10:p:1744-:d:819432. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.