scholarly journals A modified whale optimization algorithm for parameter estimation of software reliability growth models

2021 ◽  
Vol 15 ◽  
pp. 174830262110344
Author(s):  
Kezhong Lu ◽  
Zongmin Ma
Keyword(s):  
Optimization Algorithm ◽  
Software Reliability ◽  
Growth Models ◽  
Experimental Results ◽  
Whale Optimization Algorithm ◽  
Reliability Growth ◽  
Software Faults ◽  
Whale Optimization ◽  
Software Reliability Growth Models ◽  
Software Reliability Growth

Software reliability growth models are nonlinear in nature, so it is difficult to estimate the proper parameters. An estimation method based on a modified whale optimization algorithm in which parameters are estimated is discussed in this paper. The whale optimization algorithm is a new swarm intelligence optimization algorithm. This algorithm is not perfect enough. Based on the analysis of whale optimization algorithm, we point out the disadvantages of whale optimization algorithm, and propose a modified whale optimization algorithm algorithm from four aspects: choice regarding the dimension, exploration control, encircling prey modified, and candidate solution selection. The experimental results based on 34 benchmark functions demonstrate that the proposed modified whale optimization algorithm has better accuracy. The modified whale optimization algorithm is used to predict software reliability by predicting the faults during the software testing process using software faults’ historical data. The proposed modified whale optimization algorithm shows significant advantages in handling a variety of modeling problems such as the exponential model, power model, delayed s-shaped model, and modified sigmoid model. Experimental results show that the fitting accuracy of the modified sigmoid model model is minimal on three data sets. The modified whale optimization algorithm with the modified sigmoid model can provide a better estimate of the software faults.

scholarly journals Parameter Estimation Techniques of Software Reliability Growth Models: A Critical Research with Experimentation

2019 ◽  
Vol 8 (4) ◽  
pp. 7763-7770
Keyword(s):  
Parameter Estimation ◽  
Software Development ◽  
Software Reliability ◽  
Growth Models ◽  
Reliability Growth ◽  
Estimation Techniques ◽  
Testing Procedures ◽  
Software Reliability Growth Models ◽  
Software Reliability Growth ◽  
Parameter Estimation Techniques

Ensuring software reliability is a challenging task in software development. Software reliability is the probability of software to provide its intended functionality over a specified time. A couple of testing procedures during the phases of software development provides the data to approximate software reliability. This approximation guides the development team to plan necessary corrective actions. A variety of Software Reliability Growth Models (SRGMs) are in use to predict software reliability. A common task for every SRGM is to calculate reliability growth models attributes as a part of reliability estimation. Optimal calculation of such attributes is influenced by the relationships among the parameters of an SRGM. Therefore parametric SRGMs rely on parameter estimation techniques. The present paper has undertaken the study of existing parameter estimation techniques with the main goal of understanding the pros and cons of each technique in order to design a better technique of parameter estimation for SRGM’s in use. A critical review of existing techniques of parameter techniques is given in this paper detailing the categories, approaches, problems relating to the techniques. One of the most successful swam intelligence techniques named Gray Wolf Optimization (GWO) along with its variants are applied to estimate the parameters of SRGMs. The results of this application along with the comparative analysis are given. The variants of GWO played a significant role in parameter estimation of the SRGMs considered for the experiments. An attempt is made to propose new ways of parameter estimation to achieve optimization.

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