A Novel Method for Software Reliability Assessment via Neuro-Fuzzy System

Document Type : Original Research Article

Author

Department of Computer Engineering, Ayatollah Amoli Branch, Islamic Azad University, Mazandaran, Iran

Abstract

Nowadays, the utilization of software engineering in various areas of technology is remarkably increased. As a matter of fact, it is used in many critical applications such as eye surgery, autopilot systems of airplanes, centralized traffic control (CTC), and so on. Therefore, the reliability of software is very important, and it plays an essential role in the lifetime of the software. Software reliability is one of the main characteristics of software quality. Moreover, the rapid assessment of the reliability of the application is essential during the software life cycle. In this paper, Iuse the neuro-fuzzy methods to assess the software's reliability in order to cope with uncertainties in measuring the actual parameters of the software. By designing neuro-fuzzy inference systems and applying four parameters of the ISO/IEC 9126quality model(i.e., the maturity of software, fault-tolerant, recoverability, and reliability compliance) and finding the parameters of a fuzzy system by exploiting approximation techniques from neural networks, Ipresent an integrated assessment model for evaluation of software reliability. The case study used in this paper to evaluate the proposed method is the software income tax calculator. By applying the input parameters, Iobserve that the software reliability is 0.65. software reliability in our proposed method is more exact than software reliability in the fuzzy multi-criteria and fuzzy method because The weights of the input parameters have been set by experts and software developers, and simulations are carried out using MATLAB tool (ANFIS). Simulations confirm that the proposed method provides acceptable results.

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Main Subjects

References

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International Journal of Reliability, Risk and Safety: Theory and Application
Volume 5, Issue 1
June 2022
Pages 43-48

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History

  • Receive Date: 02 September 2022
  • Revise Date: 27 September 2022
  • Accept Date: 30 September 2022

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