Software engineering metrics and models
Software engineering metrics and models
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Bayesian Analysis of Empirical Software Engineering Cost Models
IEEE Transactions on Software Engineering
A replicated assessment and comparison of common software cost modeling techniques
Proceedings of the 22nd international conference on Software engineering
Tabu Search
Web Development Effort Estimation Using Analogy
ASWEC '00 Proceedings of the 2000 Australian Software Engineering Conference
A meta-model for software development resource expenditures
ICSE '81 Proceedings of the 5th international conference on Software engineering
Early Web Size Measures and Effort Prediction for Web Costimation
METRICS '03 Proceedings of the 9th International Symposium on Software Metrics
A tutorial on support vector regression
Statistics and Computing
Investigating Web size metrics for early Web cost estimation
Journal of Systems and Software
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
Comparing Size Measures for Predicting Web Application Development Effort: A Case Study
ESEM '07 Proceedings of the First International Symposium on Empirical Software Engineering and Measurement
Software Effort Estimation using Machine Learning Techniques with Robust Confidence Intervals
HIS '07 Proceedings of the 7th International Conference on Hybrid Intelligent Systems
Cross-company vs. single-company web effort models using the Tukutuku database: An extended study
Journal of Systems and Software
The Use of Bayesian Networks for Web Effort Estimation: Further Investigation
ICWE '08 Proceedings of the 2008 Eighth International Conference on Web Engineering
Bayesian Network Models for Web Effort Prediction: A Comparative Study
IEEE Transactions on Software Engineering
Applying support vector regression for web effort estimation using a cross-company dataset
ESEM '09 Proceedings of the 2009 3rd International Symposium on Empirical Software Engineering and Measurement
Using Tabu Search to Estimate Software Development Effort
IWSM '09 /Mensura '09 Proceedings of the International Conferences on Software Process and Product Measurement
Investigating the use of Support Vector Regression for web effort estimation
Empirical Software Engineering
Efficient tuning of SVM hyperparameters using radius/margin bound and iterative algorithms
IEEE Transactions on Neural Networks
A genetic algorithm to configure support vector machines for predicting fault-prone components
PROFES'11 Proceedings of the 12th international conference on Product-focused software process improvement
Systematic literature review of machine learning based software development effort estimation models
Information and Software Technology
Special issue on repeatable results in software engineering prediction
Empirical Software Engineering
Search-based approaches for software development effort estimation
Proceedings of the 12th International Conference on Product Focused Software Development and Process Improvement
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
Dynamic adaptive search based software engineering
Proceedings of the ACM-IEEE international symposium on Empirical software engineering and measurement
A grammatical evolution approach for software effort estimation
Proceedings of the 15th annual conference on Genetic and evolutionary computation
The impact of parameter tuning on software effort estimation using learning machines
Proceedings of the 9th International Conference on Predictive Models in Software Engineering
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Background. Recent studies have shown that Support Vector Regression (SVR) has an interesting potential in the field of effort estimation. However applying SVR requires to carefully set some parameters that heavily affect the prediction accuracy. No general guidelines are available to select these parameters, whose choice also depends on the characteristics of the data set used. This motivates the work described in this paper. Aims. We have investigated the use of an optimization technique in combination with SVR to select a suitable subset of parameters to be used for effort estimation. This technique is named Tabu Search (TS), which is a meta-heuristic approach used to address several optimization problems. Method. We employed SVR with linear and RBF kernels, and used variables' preprocessing strategies (i.e., logarithmic). As for the data set, we employed the Tukutuku cross-company database, which is widely adopted in Web effort estimation studies, and performed a hold-out validation using two different splits of the data set. As benchmark, results are compared to those obtained with Manual StepWise Regression, Case-Based Reasoning, and Bayesian Networks. Results. Our results show that TS provides a good choice of parameters, so that the combination of TS and SVR outperforms any other technique applied on this data set. Conclusions. The use of the meta-heuristic Tabu Search allowed us to obtain (I) an automatic choice of the parameters required to run SVR, and (II) a significant improvement on prediction accuracy for SVR. While we are not guaranteed that this is the global optimum, the results we are presenting are the best performance ever obtained on the problem at the hand, up to now. Of course, the experimental results here presented should be assessed on further data. However, they are surely interesting enough to suggest the use of SVR among the techniques that are suitable for effort estimation, especially when using a cross-company database.