Software requirements prioritization: IDOCRIW and QUALIFLEX approach

Prioritizing software requirements directly influence the quality of software projects. Developers face the challenges when working on prioritizing software requirements. In modern age agile methodology widely adopted due to its distinctive nature i.e. its nature is iterative and focusing on quality of software. The selection of software requirement is multi-criteria decision-making problem because there are different ways in market to perform this task and it depends on different competing attributes. Decision makers use the linguistic terms to show their uncertainty and subjective judgments. Although many software prioritization methods are still present but these method face challenges to incorporate the human uncertainty. To address these challenges, this study proposes a novel decision-making framework that integrates the Probabilistic Uncertain Linguistic q-Rung Orthopair Fuzzy Set to model expert uncertainty. The framework combines the Integrated Determination of Objective Criteria Weights method with the Qualitative Flexible Multiple Criteria method to enhance prioritization accuracy. Criteria weights are computed using the Probabilistic Uncertain Linguistic q-Rung Orthopair Fuzzy-Integrated Determination of Objective Criteria Weights method, and requirement selection is performed through the Probabilistic Uncertain Linguistic q-Rung Orthopair Fuzzy-Qualitative Flexible Multiple Criteria model. Four functional requirements-login credentials, parent connectivity, scheduling accuracy, and systematic evaluation- and four non-functional requirements-security, performance, usability, and reliability-were evaluated. The proposed method achieved the highest performance score of 0.4806 for alternative ϒ1, outperforming Probabilistic Uncertain Linguistic-Technique for Order Preference by Similarity to Ideal Solution and Probabilistic Uncertain Linguistic-Multi-Attributive Border Approximation Area Comparison by 321.6% and 232.0%, respectively. It also showed comparable results to Probabilistic Uncertain Linguistic-VIseKriterijumska Optimizacija I Kompromisno Resenje (9.0% difference) and was 23.3% lower than Probabilistic Uncertain Linguistic-Evaluation Based on Distance from Average Solution. Sensitivity and comparative analysis confirm that the proposed Probabilistic Uncertain Linguistic q-Rung Orthopair Fuzzy-Qualitative Flexible Multiple Criteria model provides robust, consistent, and reliable decision outcomes for software requirement selection.