Adaptive fuzzy systems and control: design and stability analysis
Adaptive fuzzy systems and control: design and stability analysis
Machine Learning Approaches to Estimating Software Development Effort
IEEE Transactions on Software Engineering
Fuzzy set theory—and its applications (3rd ed.)
Fuzzy set theory—and its applications (3rd ed.)
Calibrating the COCOMO II post-architecture model
Proceedings of the 20th international conference on Software engineering
Software cost estimation with fuzzy models
ACM SIGAPP Applied Computing Review
Software Engineering Economics
Software Engineering Economics
Search Heuristics, Case-based Reasoning And Software Project Effort Prediction
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Building A Software Cost Estimation Model Based On Categorical Data
METRICS '01 Proceedings of the 7th International Symposium on Software Metrics
Bayesian analysis of software cost and quality models
Bayesian analysis of software cost and quality models
A Systematic Review of Software Development Cost Estimation Studies
IEEE Transactions on Software Engineering
A General Empirical Solution to the Macro Software Sizing and Estimating Problem
IEEE Transactions on Software Engineering
Information and Software Technology
A method of programming measurement and estimation
IBM Systems Journal
Probabilistic size proxy for software effort prediction: A framework
Information and Software Technology
Information and Software Technology
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Reliable effort prediction remains an ongoing challenge to software engineers. Traditional approaches to effort prediction such as the use of models derived from historical data, or the use of expert opinion are plagued with issues pertaining to their effectiveness and robustness. These issues are more pronounced when the effort prediction is used during the early phases of the software development lifecycle. Recent works have demonstrated promising results obtained with the use of fuzzy logic. Fuzzy logic based effort prediction systems can deal better with imprecision, which characterizes the early phases of most software development projects, for example requirements development, whose effort predictors along with their relationships to effort are characterized as being even more imprecise and uncertain than those of later development phases, for example design. Fuzzy logic based prediction systems could produce further better estimates provided that various parameters and factors pertaining to fuzzy logic are carefully set. In this paper, we present an empirical study, which shows that the prediction accuracy of a fuzzy logic based effort prediction system is highly dependent on the system architecture, the corresponding parameters, and the training algorithms.