Uncertainty management in software effort estimation using a consistent fuzzy analogy-based method

摘要

Software effort estimation is a critical task in software project development management. Unfortunately, the uncertainty and inaccuracy are inherent properties of the software effort estimation environment. These are caused by the limited capabilities of the managers, to foresee, measure and describe factors influencing the software effort. The promising Fuzzy Analogy-based Software Effort Estimation model (FASEE) employs successfully fuzzy logic with approximate reasoning theory to handle imprecision and reasoning under uncertainty. Also, FASEE use possibility distribution to quantify the uncertainty in the estimate that aid the software managers to assess risks. Yet, the FASEE suffer from the low data quality and the uncertainty induced in the reasoning process. In this paper, we propose an enhancement of the FASEE, by imposing consistency criteria to deal with the aforementioned drawbacks. So, the underlying model, called Consistent Fuzzy Analogy-based Software Effort Estimation (C-FASEE) is endowed with two capabilities. The first one introduces consistency criteria in attribute representation by fuzzy sets to enable fitting each attribute to the software effort. The second one introduces a new relation of confidence to measure the extent that the resulted most similar projects respect the assumption "similar projects have similar efforts". Moreover, the C-FASEE method provide a fuzzy estimate of the most possible fuzzy set will the true effort of the new software project falls in. This allow to the software manager to assess risks more optimally. The proposed C-FASEE is validated over thirteen software project datasets that represent different complexities. The obtained results are compared to variant methods of the analogy based software effort estimation approach. The experimental results show that our proposal provides a good estimation accuracy of and has significantly best performance against the comparison methods. (C) 2018 Elsevier B.V. All rights reserved.