Software Reliability Growth Model Selection by Using VIKOR Method Based on q-Rung Orthopair Fuzzy Entropy and Divergence Measures

Abstract

Software reliability growth models (SRGMs) are widely used for predicting the reliability of software systems during testing and debugging. Selecting the most appropriate SRGM solutions is a multi-criteria decision-making (MCDM) problem that is very difficult when criteria are imprecise or ambiguous. The paper introduces a novel approach to SRGM selection which relies on q-rung orthopair fuzzy sets (q-ROFS) and the compromise ranking method of VIKOR. New q-rung orthopair fuzzy (q ROF) entropy and divergence measures are proposed for criteria weights assignment and to select superior SRGMs. The VIKOR method is then applied on the q-ROF decision matrix to identify the optimal compromise SRGM solution. This approach provides a systematic framework for handling subjective criteria and modeling uncertainty during SRGM selection. The proposed MCDM methodology is illustrated on the example of a case study involving four common SRGMs evaluated on the four different criteria. Results are demonstrated to be in line with the latest q-rung measures which provide more accurate results than the previous intuitionistic fuzzy methods. The q-ROF VIKOR approach provides the software teams with a more robust information base for the reliability growth decision-making process. At the end of this manuscript, we do the comparison of the proposed theory with certain prevailing concepts to reveal the dominance and supremacy of this work. Whereas yet there are some expected limitations of the proposed work for instance it can't be helpful in the generalized structures of q-ROFS.