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Evaluating the Impact of Prediction Techniques: Software Reliability Perspective

Kavita Sahu1, Fahad A. Alzahrani2, R. K. Srivastava1, Rajeev Kumar3,4,*

1 Department of Computer Science, Dr. Shakuntala Misra National Rehabilitation University, Lucknow, 226017, India
2 Department of Computer Engineering, College of Computer and Information Systems, Umm Al-Qura University, Makkah, 21955, Saudi Arabia
3 Department of Computer Application, Shri Ramswaroop Memorial University, Barabanki, 225003, India
4 Department of Information Technology, Babasaheb Bhimrao Ambedkar University, Lucknow, 226025, India

* Corresponding Author: Rajeev Kumar. Email: email

Computers, Materials & Continua 2021, 67(2), 1471-1488. https://doi.org/10.32604/cmc.2021.014868

Abstract

Maintaining software reliability is the key idea for conducting quality research. This can be done by having less complex applications. While developers and other experts have made significant efforts in this context, the level of reliability is not the same as it should be. Therefore, further research into the most detailed mechanisms for evaluating and increasing software reliability is essential. A significant aspect of growing the degree of reliable applications is the quantitative assessment of reliability. There are multiple statistical as well as soft computing methods available in literature for predicting reliability of software. However, none of these mechanisms are useful for all kinds of failure datasets and applications. Hence finding the most optimal model for reliability prediction is an important concern. This paper suggests a novel method to substantially pick the best model of reliability prediction. This method is the combination of analytic hierarchy method (AHP), hesitant fuzzy (HF) sets and technique for order of preference by similarity to ideal solution (TOPSIS). In addition, using the different iterations of the process, procedural sensitivity was also performed to validate the findings. The findings of the software reliability prediction models prioritization will help the developers to estimate reliability prediction based on the software type.

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APA Style
Sahu, K., Alzahrani, F.A., Srivastava, R.K., Kumar, R. (2021). Evaluating the impact of prediction techniques: software reliability perspective. Computers, Materials & Continua, 67(2), 1471-1488. https://doi.org/10.32604/cmc.2021.014868
Vancouver Style
Sahu K, Alzahrani FA, Srivastava RK, Kumar R. Evaluating the impact of prediction techniques: software reliability perspective. Comput Mater Contin. 2021;67(2):1471-1488 https://doi.org/10.32604/cmc.2021.014868
IEEE Style
K. Sahu, F.A. Alzahrani, R.K. Srivastava, and R. Kumar "Evaluating the Impact of Prediction Techniques: Software Reliability Perspective," Comput. Mater. Contin., vol. 67, no. 2, pp. 1471-1488. 2021. https://doi.org/10.32604/cmc.2021.014868

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cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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