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<p>Selecting the most suitable software requirements is crucial for project success, but remains challenging due to inherent uncertainty, complexity, and conflicting evaluation criteria. Traditional prioritization approaches often fail to capture hesitation in decision-making, leading to suboptimal outcomes adequately. To address this, we propose a novel decision-making framework that integrates Intuitionistic Fuzzy Sets (IFS) with the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The approach effectively models membership, non-membership, and hesitation degrees, thereby providing a richer representation of uncertainty. A structured methodology is presented, including criteria definition, weight determination, intuitionistic fuzzy decision matrix construction, and TOPSIS-based ranking. A case study on an Institute Examination System demonstrates the framework’s applicability and effectiveness, showing consistent ranking results with reduced execution time compared to Fuzzy TOPSIS. The proposed method offers a more accurate and efficient mechanism for software requirements selection, supporting informed and reliable decision-making.</p>

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A holistic strategy for choosing software requirements through multi-criteria intuitionistic fuzzy TOPSIS

  • 期刊论文

  • 发表日期：

    2026年2月18日

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  • Mohd. Nazim,
  • Shahnawaz Ahmad,
  • Mohd. Aquib Ansari,
  • Mohd. Arif,
  • Javed Ahmad,
  • Safdar Tanweer

摘要

Selecting the most suitable software requirements is crucial for project success, but remains challenging due to inherent uncertainty, complexity, and conflicting evaluation criteria. Traditional prioritization approaches often fail to capture hesitation in decision-making, leading to suboptimal outcomes adequately. To address this, we propose a novel decision-making framework that integrates Intuitionistic Fuzzy Sets (IFS) with the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The approach effectively models membership, non-membership, and hesitation degrees, thereby providing a richer representation of uncertainty. A structured methodology is presented, including criteria definition, weight determination, intuitionistic fuzzy decision matrix construction, and TOPSIS-based ranking. A case study on an Institute Examination System demonstrates the framework’s applicability and effectiveness, showing consistent ranking results with reduced execution time compared to Fuzzy TOPSIS. The proposed method offers a more accurate and efficient mechanism for software requirements selection, supporting informed and reliable decision-making.