An empirical validation of software cost estimation models
Communications of the ACM
Software engineering metrics and models
Software engineering metrics and models
A Procedure for Analyzing Unbalanced Datasets
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
A Controlled Experiment to Assess the Benefits of Estimating with Analogy and Regression 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
Web Engineering: The Developers' View and a Practitioner's Approach
Web Engineering, Software Engineering and Web Application Development
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
How Valuable is company-specific Data Compared to multi-company Data for Software Cost Estimation?
METRICS '02 Proceedings of the 8th International Symposium on Software Metrics
Further Comparison of Cross-Company and Within-Company Effort Estimation Models for Web Applications
METRICS '04 Proceedings of the Software Metrics, 10th International Symposium
Software Productivity Measurement Using Multiple Size Measures
IEEE Transactions on Software Engineering
METRICS '05 Proceedings of the 11th IEEE International Software Metrics Symposium
Effort estimation modeling techniques: a case study for web applications
ICWE '06 Proceedings of the 6th international conference on Web engineering
Cross-company and single-company effort models using the ISBSG database: a further replicated study
Proceedings of the 2006 ACM/IEEE international symposium on Empirical 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
A COSMIC-FFP approach to predict web application development effort
Journal of Web 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
Cross-company vs. single-company web effort models using the Tukutuku database: An extended study
Journal of Systems and Software
A replicated study comparing web effort estimation techniques
WISE'07 Proceedings of the 8th international conference on Web information systems engineering
Software project effort assessment
Journal of Software Maintenance and Evolution: Research and Practice
Web effort estimation: the value of cross-company data set compared to single-company data set
Proceedings of the 8th International Conference on Predictive Models in Software Engineering
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Previous studies comparing the prediction accuracy of effort models built using Web cross- and single-company data sets have been inconclusive, and as such replicated studies are necessary to determine under what circumstances a company can place reliance on a cross-company effort model. This paper therefore replicates a previous study by investigating how successful a cross-company effort model is: i) to estimate effort for Web projects that belong to a single company and were not used to build the cross-company model; ii) compared to a single-company effort model. Our single-company data set had data on 15 Web projects from a single company and our cross-company data set had data on 68 Web projects from 25 different companies. The effort estimates used in our analysis were obtained by means of two effort estimation techniques, namely forward stepwise regression and case-based reasoning. Our results were similar to those from the replicated study, showing that predictions based on the single-company model were significantly more accurate than those based on the cross-company model.