A comparison between fuzzy AHP and fuzzy TOPSIS methods to software requirements selection

The fuzzy set theory as one of the key agents of artificial intelligence has been used to deal with vagueness and imprecision during the decision-making process. The software requirements selection is a multicriteria decision making problem which has a key importance for several software development companies. Few methods have been developed to select the software requirements from the list of the elicited requirements using fuzzy analytic hierarchy process (AHP) and fuzzy technique for order of preference by similarity to ideal solution (TOPSIS) methods. Based on our review, we found that little attention is given on the comparison between the fuzzy AHP and fuzzy TOPSIS methods in the context of the software requirements selection problem. To address this issue, this paper presents a comparative analysis of fuzzy AHP and fuzzy TOPSIS methods by considering the small and large set of requirements of an institute examination system based on the following factors: agreement measure, time complexity, rank reversal issue, and number of judgments by decision makers. Based on the comparative study, we found that both fuzzy AHP and fuzzy TOPSIS methods produce the same set of functional requirements based on agreement measure metric in both dataset-1 and dataset-2. Fuzzy AHP requires less time to generate the ranking order in dataset-1; and fuzzy TOPSIS performs better then fuzzy AHP in dataset-2. Fuzzy AHP causes the rank reversal issue; and there is no rank reversal issue in fuzzy TOPSIS and it produces the consistent results. Fuzzy TOPSIS requires less judgment by decision makers then fuzzy AHP. Finally, we discuss the future research directions in the area of SRs selection problem.