Multi Criteria Decision Making Approach for Selecting Effort Estimation Model

Abstract

Effort Estimation has always been a challenging task for the Project managers. Many researchers have tried to help them by creating different types of models. This has been already proved that none is successful for all types of projects and every type of environment. Analytic Hierarchy Process (AHP) has been identified as the tool that would help in Multi Criteria Decision Making. Researchers have identified that AHP can be used for the comparison of effort estimation of different models and techniques. But the problem with traditional AHP is its inability to deal with the imprecision and subjectivity in the pairwise comparison process. The motive of this paper is to propose Fuzzy Analytic Hierarchy Process, which can be used to rectify the subjectivity and imprecision of AHP and can be used for selecting the type of Model best suited for estimating the effort for a given problem type or environment. Instead of single crisp value, Fuzzy AHP uses a range of values to incorporate decision maker"s uncertainty. From this range, decision maker can select the value that reflects his confidence and also he can specify his attitude like optimistic, pessimistic or moderate. In this work, the comparison of AHP and Fuzzy AHP is concluded using a case study of selection of effort estimation model.

Land suitability analysis for rice cultivation using a GIS-based fuzzy multi-criteria decision making approach: central part of Amol District, Iran

“…With regard to the previous studies, one of the disadvantages of using multi-criteria decision-making methods through the use of classic AHP is the uncertainty in selecting the value of the criteria in relation to one another in pair-wise comparison (a number between 1 and 9) (Sehra et al 2012;Velasquez & Hester 2013;Konan-Waidhet et al 2015).…”

Applications of Multi-criteria Decision Making in Software Engineering

“…Weights are assigned to each node and many pairwise comparisons and matrix multiplications are made assessing the relative importance of these criteria. The end result of this method is to provide a formal, systematic means of extracting, combining, and capturing expert judgements and their relationship to analogous reference data [10].…”

“…The range values can be given as optimistic, pessimistic or moderate [10]. For finding the weights the extent analysis method given by Wang et al [15] is utilized.…”

Multi-criteria decision making approach for the selection of software effort estimation model

“…In the contemporary work, Kaur et al (2010) used some attributes as mean magnitude of relative error (MMRE), mean square error (MSE), root mean square error (RMSE) and root mean square error (RMSSE) as the selection criteria. Sehra et al (2013) proposed a model based on Fuzzy Analytic Hierarchy Process by accounting reliability (REL), mean magnitude of relative error (MMRE), Prediction (PRE) and uncertainty (UNC) as selection criteria. Malathi and Sridhar (2012) proposed different selection criteria for the selection and raking of effort estimation models such as prediction (PRED), value accounted for (VAF), variance absolute relative error (VARE), mean absolute relative error (MARE), magnitude of relative error (MRE), root mean square error (RMSE).…”