Software project development cost estimation
Journal of Systems and Software
An empirical validation of software cost estimation models
Communications of the ACM
Software engineering metrics and models
Software engineering metrics and models
Machine Learning Approaches to Estimating Software Development Effort
IEEE Transactions on Software Engineering
Estimating Software Project Effort Using Analogies
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 replicated assessment and comparison of common software cost modeling techniques
Proceedings of the 22nd international conference on Software engineering
Software Engineering Economics
Software Engineering Economics
Software Cost Estimation with Cocomo II with Cdrom
Software Cost Estimation with Cocomo II with Cdrom
Human Performance Estimating with Analogy and Regression Models: An Empirical Validation
METRICS '98 Proceedings of the 5th 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
A Comparison of Development Effort Estimation Techniques for Web Hypermedia Applications
METRICS '02 Proceedings of the 8th 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
A Simulation Study of the Model Evaluation Criterion MMRE
IEEE Transactions on Software Engineering
Software effort estimation by analogy and "regression toward the mean"
Journal of Systems and Software - Special issue: Best papers on Software Engineering from the SEKE'01 Conference
Further Comparison of Cross-Company and Within-Company Effort Estimation Models for Web Applications
METRICS '04 Proceedings of the Software Metrics, 10th International Symposium
Feature subset selection can improve software cost estimation accuracy
PROMISE '05 Proceedings of the 2005 workshop on Predictor models in software engineering
METRICS '05 Proceedings of the 11th IEEE International Software Metrics Symposium
The adjusted analogy-based software effort estimation based on similarity distances
Journal of Systems and Software
A Systematic Review of Software Development Cost Estimation Studies
IEEE Transactions on Software Engineering
Cross versus Within-Company Cost Estimation Studies: A Systematic Review
IEEE Transactions on Software Engineering
Replicating studies on cross- vs single-company effort models using the ISBSG Database
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
Adaptive ridge regression system for software cost estimating on multi-collinear datasets
Journal of Systems and Software
Proceedings of the 6th International Conference on Predictive Models in Software Engineering
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Precision in estimating the required software development effort plays a critical factor in the success of software project management. Most existing software effort estimation models only compare the accuracies of software effort estimates from the historical data without clustering. A potential factor that can affect the accuracies of the established effort estimation models is the homogeneity of the data. However, such investigation on the effects of the accuracies of the derived effort estimates is seldom explored in software effort estimation literature. Therefore, this paper aims to explore the effects of accuracies of the software effort estimation models established from the clustered data by using the International Software Benchmarking Standards Group (ISBSG) repository. The ordinary least square (OLS) regression method is adopted to establish a respective effort estimation model in each cluster of datasets. The empirical experiment results show that the estimation accuracies do not reveal significant differences within the respective dataset clustered by each software effort driver. It also demonstrates that software effort estimation models from the clustered data present almost similar accuracy results compared to models from the entire data without clustering.