Software engineering metrics and models
Software engineering metrics and models
The nature of statistical learning theory
The nature of statistical learning theory
A Procedure for Analyzing Unbalanced Datasets
IEEE Transactions on Software Engineering
Making large-scale support vector machine learning practical
Advances in kernel methods
Support vector machines for dynamic reconstruction of a chaotic system
Advances in kernel methods
Bayesian Analysis of Empirical Software Engineering Cost Models
IEEE Transactions on Software Engineering
Web Metrics Estimating Design and Authoring Effort
IEEE MultiMedia
Web Development: Estimating Quick-to-Market Software
IEEE Software
A Comparative Study of Cost Estimation Models for Web Hypermedia Applications
Empirical Software Engineering
Web Engineering: The Developers' View and a Practitioner's Approach
Web Engineering, Software Engineering and Web Application Development
Cost estimation for web applications
Proceedings of the 25th International Conference on Software Engineering
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
Using Simulation to Evaluate Prediction Techniques
METRICS '01 Proceedings of the 7th International Symposium on Software Metrics
Using Public Domain Metrics To Estimate Software Development Effort
METRICS '01 Proceedings of the 7th International Symposium on Software Metrics
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
Further Comparison of Cross-Company and Within-Company Effort Estimation Models for Web Applications
METRICS '04 Proceedings of the Software Metrics, 10th International Symposium
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
Cross versus Within-Company Cost Estimation Studies: A Systematic Review
IEEE Transactions on Software Engineering
Three empirical studies on estimating the design effort of Web applications
ACM Transactions on Software Engineering and Methodology (TOSEM)
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
A systematic review of cross- vs. within- company cost estimation studies
EASE'06 Proceedings of the 10th international conference on Evaluation and Assessment in Software Engineering
A model-driven measurement procedure for sizing web applications: design, automation and validation
MODELS'07 Proceedings of the 10th international conference on Model Driven Engineering Languages and Systems
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The objective of this paper is to investigate the use of Support Vector Regression (SVR) for Web development effort estimation when using a cross-company data set. Four kernels of SVR were used, linear, polynomial, Gaussian and sigmoid and two preprocessing strategies of the variables were applied, namely normalization and logarithmic. The hold-out validation process was carried out for all the eight configurations using a training set and a validation set from the Tukutuku data set. Our results suggest that the predictions obtained with linear kernel applying a logarithmic transformation of variables (LinLog) are significantly better than those obtained with the other configurations. In addition, SVR has been compared with the traditional estimation techniques, such as Manual StepWise Regression, Case-Based Reasoning, and Bayesian Networks. Our results suggest that SVR with LinLog configuration can provide significantly superior prediction accuracy than other techniques.