The Compatibility of Parametric Software ‎Reliability Growth Models in PRGA

Subject Areas : Computer Engineering

Reza Roshani ¹ , Homayun Motameni ² , Hosein Mohamadi ³

1 - Department of Computer Engineering, Sari Branch, Islamic Azad University, Sari, Iran
2 - Department of Computer Engineering, Sari Branch, Islamic Azad University, Sari, Iran
3 - Department of Computer Engineering, Azadshar Branch, Islamic Azad University, Azadshar, Iran

Received: 2023-02-11 Accepted : 2023-03-09 Published : 2023-06-01

Keywords: Genetic Algorithm, compatibility, software reliability, SR Growth Model,

Abstract :

Software Reliability (SR) is a key non-operational feature measured when evaluating software quality. To enhance this feature, it is important to detect failures and mitigate them in the testing phase. SR can be increased by identifying and removing this failure from the defect data. The existing literature consists of many models/methods applicable to measuring SR, including the SR Growth Models (SRGMs). Generally, SRGMs are in two main types: parametric and non-parametric. As these models are diverse, when applying to a certain problem, the particular requirements and conditions of that problem should be taken into account. The current paper explains the fundamental concepts of reliability, then reviews the Parametric SR Growth Models (PSRGMs) and evaluates various approaches already proposed in this domain. In addition, this study investigates the SRGMs compatibility by means of a novel Parallel Real-valued Genetic Algorithm (PRGA)-based method. The results achieved under a variety of conditions for each model showed the extent of compatibility with GA.

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