Grammatical Evolution: Evolving Programs for an Arbitrary Language
EuroGP '98 Proceedings of the First European Workshop on Genetic Programming
A Probabilistic Model for Predicting Software Development Effort
IEEE Transactions on Software Engineering
Effort estimation modeling techniques: a case study for web applications
ICWE '06 Proceedings of the 6th international conference on Web engineering
The adjusted analogy-based software effort estimation based on similarity distances
Journal of Systems and Software
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
A Systematic Review of Software Development Cost Estimation Studies
IEEE Transactions on Software Engineering
Software Effort Estimation Using Machine Learning Techniques with Robust Confidence Intervals
ICTAI '07 Proceedings of the 19th IEEE International Conference on Tools with Artificial Intelligence - Volume 01
Bayesian Network Models for Web Effort Prediction: A Comparative Study
IEEE Transactions on Software Engineering
Comparison of estimation methods of cost and duration in IT projects
Information and Software Technology
Improved estimation of software project effort using multiple additive regression trees
Expert Systems with Applications: An International Journal
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Issues on Estimating Software Metrics in a Large Software Operation
SEW '08 Proceedings of the 2008 32nd Annual IEEE Software Engineering Workshop
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
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Introducing a Perl genetic programming system - and can meta-evolution solve the bloat problem?
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
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
Applying Genetic Programming for Estimating Software Development Effort of Short-scale Projects
ITNG '11 Proceedings of the 2011 Eighth International Conference on Information Technology: New Generations
IEEE Transactions on Evolutionary Computation
Predicting software maintenance effort through evolutionary-based decision trees
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Single and Multi Objective Genetic Programming for software development effort estimation
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Software effort prediction: a hyper-heuristic decision-tree based approach
Proceedings of the 28th Annual ACM Symposium on Applied Computing
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Software effort estimation is an important task within software engineering. It is widely used for planning and monitoring software project development as a means to deliver the product on time and within budget. Several approaches for generating predictive models from collected metrics have been proposed throughout the years. Machine learning algorithms, in particular, have been widely-employed to this task, bearing in mind their capability of providing accurate predictive models for the analysis of project stakeholders. In this paper, we propose a grammatical evolution approach for software metrics estimation. Our novel algorithm, namely SEEGE, is empirically evaluated on public project data sets, and we compare its performance with state-of-the-art machine learning algorithms such as support vector machines for regression and artificial neural networks, and also to popular linear regression. Results show that SEEGE outperforms the other algorithms considering three different evaluation measures, clearly indicating its effectiveness for the effort estimation task.