Algorithmic cost estimation for software evolution
Proceedings of the 22nd international conference on Software engineering
Software cost estimation with fuzzy models
ACM SIGAPP Applied Computing Review
Software Engineering Economics
Software Engineering Economics
Software Development Cost Estimation Using Function Points
IEEE Transactions on Software Engineering
Using Public Domain Metrics To Estimate Software Development Effort
METRICS '01 Proceedings of the 7th International Symposium on Software Metrics
Building A Software Cost Estimation Model Based On Categorical Data
METRICS '01 Proceedings of the 7th International Symposium on Software Metrics
A Review of Surveys on Software Effort Estimation
ISESE '03 Proceedings of the 2003 International Symposium on Empirical Software Engineering
A soft computing framework for software effort estimation
Soft Computing - A Fusion of Foundations, Methodologies and Applications
A General Empirical Solution to the Macro Software Sizing and Estimating Problem
IEEE Transactions on Software Engineering
IEEE Transactions on Software Engineering
A method of programming measurement and estimation
IBM Systems Journal
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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).