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Numerous factors, including project complexity, team size, customer collaboration, and development pace, must be taken into consideration while choosing a software development methodology. This problem is a multi-criteria decision-making dilemma since the many criteria frequently fight with one another and fluctuate in priority. To systematically select the best methodology, the multi-criteria decision-making technique assists in quantifying and assessing these factors. This ensures that decisions are made in a fair and informed manner within the context of the challenging software development environment. Thus, in this article, we develop a multi-criteria decision-making approach in the setting of bipolar complex fuzzy information for the prioritization and selection of optimal software development methodology. For this, we first, invent logarithmic operational laws and associated results for the bipolar complex fuzzy set. Then, we invent four aggregation operators by utilizing these logarithmic operational laws under the structure of bipolar complex fuzzy information, that is, logarithmic bipolar complex fuzzy weighted averaging, logarithmic bipolar complex fuzzy ordered weighted averaging, logarithmic bipolar complex fuzzy weighted geometric, and logarithmic bipolar complex fuzzy ordered weighted geometric. After that, we solve a multi-criteria decision-making dilemma related to the prioritization and selection of software development methodology by considering the artificial data in the setting of bipolar complex fuzzy information and achieve that &#x201C;Agile&#x201D; is the optimal software development methodology among the considered four different software development methodologies, i.e., Waterfall, DevOps, Spiral, and Agile. In the last, we investigate the comparison study of the deduced theory to a few current theories to reveal the importance and supremacy of the constructed theory.

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Selection of Software Development Methodology by Employing a Multi-Criteria Decision-Making Approach based on Logarithmic Bipolar Complex Fuzzy Aggregation Operators