The selection of requirements for an Information System (IS) based on their ranking value constitutes a systematic and methodical approach to identifying and prioritizing the most critical system requirements. These requirements have been identified through the consensus of stakeholders actively involved in the development of the IS. The process of selecting IS requirements based on distinct evaluation criteria employs a Multi-Criteria Decision-Making (MCDM) approach. Among the various MCDM methods, the “Technique for Order of Preference by Similarity to Ideal Solution” (TOPSIS) is frequently employed in the literature on IS development, as it involves fewer computational operations for determining the ranking values of requirements and effectively addresses the rank reversal issue. Various normalization procedures are used in TOPSIS during the computational process. Existing methods based on TOPSIS for selecting the requirements do not support the analysis of various normalization procedures of fuzzy TOPSIS, i.e., Linear Scale Transformation (LST) including LST(Max), LST(Sum), LST(Max–Min), and vector normalization during software requirements selection process. Therefore, to address this issue, the ranking order produced by fuzzy Analytic Hierarchy Process (AHP) and fuzzy TOPSIS with LST (Max), LST (Sum), LST (Max–Min), and vector normalization are compared and analyzed. The requirements of an Institute Examination System (IES) have been utilized in the experimental study, and the resulting outcomes have been analyzed using Spearman’s rank correlation coefficient.