Software engineering metrics and models
Software engineering metrics and models
Genetic programming (videotape): the movie
Genetic programming (videotape): the movie
Robust regression for developing software estimation models
Journal of Systems and Software
Estimating Software Project Effort Using Analogies
IEEE Transactions on Software Engineering
An assessment and comparison of common software cost estimation modeling techniques
Proceedings of the 21st international conference on Software engineering
Modeling Development Effort in Object-Oriented Systems Using Design Properties
IEEE Transactions on Software Engineering - Special section on the seventh international software metrics symposium
Case Studies for Method and Tool Evaluation
IEEE Software
Proceedings of the 3rd International Conference on Genetic Algorithms
Metrics Are Fitness Functions Too
METRICS '04 Proceedings of the Software Metrics, 10th International Symposium
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Selecting Best Practices for Effort Estimation
IEEE Transactions on Software Engineering
The Current State and Future of Search Based Software Engineering
FOSE '07 2007 Future of Software Engineering
The multi-objective next release problem
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Bayesian Network Models for Web Effort Prediction: A Comparative Study
IEEE Transactions on Software Engineering
Using genetic programming to improve software effort estimation based on general data sets
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
How effective is Tabu search to configure support vector regression for effort estimation?
Proceedings of the 6th International Conference on Predictive Models in Software Engineering
Genetic Programming for Effort Estimation: An Analysis of the Impact of Different Fitness Functions
SSBSE '10 Proceedings of the 2nd International Symposium on Search Based Software Engineering
IEEE Transactions on Software Engineering
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
A grammatical evolution approach for software effort estimation
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Proceedings of the 9th International Conference on Predictive Models in Software Engineering
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The idea of exploiting Genetic Programming (GP) to estimate software development effort is based on the observation that the effort estimation problem can be formulated as an optimization problem. Indeed, among the possible models, we have to identify the one providing the most accurate estimates. To this end a suitable measure to evaluate and compare different models is needed. However, in the context of effort estimation there does not exist a unique measure that allows us to compare different models but several different criteria (e.g., MMRE, Pred(25), MdMRE) have been proposed. Aiming at getting an insight on the effects of using different measures as fitness function, in this paper we analyzed the performance of GP using each of the five most used evaluation criteria. Moreover, we designed a Multi-Objective Genetic Programming (MOGP) based on Pareto optimality to simultaneously optimize the five evaluation measures and analyzed whether MOGP is able to build estimation models more accurate than those obtained using GP. The results of the empirical analysis, carried out using three publicly available datasets, showed that the choice of the fitness function significantly affects the estimation accuracy of the models built with GP and the use of some fitness functions allowed GP to get estimation accuracy comparable with the ones provided by MOGP.