An empirical validation of software cost estimation models
Communications of the ACM
Software engineering metrics and models
Software engineering metrics and models
An assessment and comparison of common software cost estimation modeling techniques
Proceedings of the 21st international conference on Software engineering
A replicated assessment and comparison of common software cost modeling techniques
Proceedings of the 22nd 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
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
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
Proceedings of the 16th international conference on World Wide Web
A Systematic Review of Software Development Cost Estimation Studies
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
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
Requirements for Rich Internet Application Design Methodologies
WISE '08 Proceedings of the 9th international conference on Web Information Systems 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 Support Vector Regression for Web Development Effort Estimation
IWSM '09 /Mensura '09 Proceedings of the International Conferences on Software Process and Product Measurement
Visual comparison of software cost estimation models by regression error characteristic analysis
Journal of Systems and Software
ACSC '09 Proceedings of the Thirty-Second Australasian Conference on Computer Science - Volume 91
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
Investigating the use of Support Vector Regression for web effort estimation
Empirical Software Engineering
An empirical evaluation of outlier deletion methods for analogy-based cost estimation
Proceedings of the 7th International Conference on Predictive Models in Software Engineering
Systematic literature review of machine learning based software development effort estimation models
Information and Software Technology
Empirical Software Engineering
EASE'09 Proceedings of the 13th international conference on Evaluation and Assessment in Software Engineering
EASE'08 Proceedings of the 12th international conference on Evaluation and Assessment in Software Engineering
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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In 2004 [Kitchenham, B.A., Mendes, E., 2004a. Software productivity measurement using multiple size measures. IEEE Transactions on Software Engineering 30 (12), 1023-1035, Kitchenham, B.A., Mendes, E., 2004b. A comparison of cross-company and single-company effort estimation models for web applications. In: Proceedings Evaluation and Assessment in Software Engineering (EASE' 04), pp. 47-55] (S1) investigated, using data on 63 Web projects, to what extent a cross-company cost model could be successfully employed to estimate development effort for single-company Web projects. Their effort models were built using Forward Stepwise Regression (SWR) and they found that cross-company predictions were significantly worse than single-company predictions. This study S1 was extended by Mendes and Kitchenham [Mendes, E., Kitchenham, B.A., 2004. Further comparison of cross-company and within company effort estimation models for web applications. In: Proceedings International Software Metrics Symposium (METRICS'04), Chicago, Illinois, September 11-17th, 2004. IEEE Computer Society, pp. 348-357] (S2), who used SWR and Case-based reasoning (CBR), and data on 67 Web projects from the Tukutuku database. They built two cross-company and one single-company models and found that both SWR cross-company models and CBR cross-company data provided predictions significantly worse than single-company predictions. Since 2004 another 83 projects were volunteered to the Tukutuku database, and recently used by Mendes et al. [Mendes, E., Di Martino, S., Ferrucci, F., Gravino, C., in press. Effort estimation: How valuable is it for a web company to use a cross-company data set, compared to using its own single-company data set? In: Proceedings of International World Wide Web Conference (WWW'07), Banff, Canada, 8-12 May] (S3), who partially replicated Mendes and Kitchenham's study (S2), using SWR and CBR. They corroborated some of S2's findings (SWR cross-company model and the CBR cross-company data provided predictions significantly worse than single-company predictions) however they replicated only part of S2. The objective of this paper (S4) is therefore to extend Mendes et al.'s work and fully replicate S2. We used the same dataset used in S3, and our results corroborated most of those obtained in S2. The main difference between S2 and our study was that one of our SWR cross-company models showed significantly similar predictions to the single-company model, which contradicts the findings from S2.