A large number of software reliability growth models (SRGMs) have been studied to estimate the reliability of software systems over the past 40 years. Different models have been developed upon different sets of assumptions. A few models were developed in a practical environment by considering testing effort, testing coverage, time delay fault correction, and fault reduction factor. Generally, SRGMs are not dataset independent, and thus the selection of an appropriate SRGM for use in a specific application is a challenging task in the software reliability area. The wrong selection of SRGMs might create a wrong estimation of reliability and consequently a delay in the release of the software. To overcome this problem, we have proposed a unique hybrid entropy weight‐based multi‐criteria decision‐making (MCDM) method and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) approach for the selection of a suitable SRGM and applied it for optimal selection and ranking of SRGMs. The proposed hybrid approach identifies the need of the relative importance of criteria for a given application without which inter‐criterion comparison cannot be accomplished. It requires a set of model selection criteria along with a set of SRGMs and their level of criteria for optimal selection. It successfully displays the result in terms of a merit value which is used to rank the SRGMs. The proposed approach has been validated on two data sets of the real software failure. The results of the study play a vital role for the decision maker to judge the suitability of the SRGM.
Various Software Reliability Growth Models (SRGMs) have been developed over last five decades to estimate the failure rate, software reliability and remaining number of faults in the software during testing phase of software development life cycle. There are numerous SRGMs available in the research literature and in the presence of numerous SRGM, appropriate selection is a staggering task for the software testing professionals. In the software reliability area, researchers have always been interested to select the optimal SRGM to use in a particular situation. In this study, we have developed an integrated technique based on Criteria Importance Through Inter Criteria Correlation (CRITIC) and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) to select the optimal SRGM. The weights of criteria are obtained by the CRITIC method and SRGMs are ranked by order using TOPSIS method. The working of proposed integrated CRITIC‐TOPSIS approach is illustrated on the two real‐time failure data sets. Furthermore, the methodology validation idea supports the current study by comparing the results obtained with the previous multi‐criteria decision making (MCDM) methodologies namely Additive Ratio Assessment (ARAS). The study revealed that the ranking of SRGMs can be solved effectively using the integration of CRITIC and TOPSIS. The result of this study plays a significant role to take decisions and to evaluate the feasibility of SRGM.
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