Abstract
Accurate and credible software effort estimation is always a challenge for academic research and software industry. In the beginning, estimation was carried out using only human expertise or algorithmic models, but more recently, interest has turned to a range of Soft Computing techniques. New paradigms such as Fuzzy Logic enable a choice for software effort estimation. Constructive Cost Model (COCOMO) is considered to be the most widely used model for effort estimation. Effort drivers have immense influence on COCOMO and this paper investigates the role of cost drivers (effort features) in improving the precision of effort estimation using Fuzzy Logic. Fuzzy logic-based estimation models are more appropriate when indistinct and incorrect information is to be used. This paper aims at estimating effort in an efficient way using a Fuzzy technique. For this purpose, the COCOMO81 dataset and the Fuzzy Inference System (FIS) of MATLAB are used for implementation. At the end, the outcomes are compared against traditional methods using parameters like Mean Magnitude of Relative Error (MMRE) and Pred (25).
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