Software engineering metrics and models
Software engineering metrics and models
Software Cost Estimation with Cocomo II with Cdrom
Software Cost Estimation with Cocomo II with Cdrom
Disaggregating and Calibrating the CASE Tool Variable in COCOMO II
IEEE Transactions on Software Engineering
An Empirical Validation of the Relationship Between the Magnitude of Relative Error and Project Size
METRICS '02 Proceedings of the 8th International Symposium on Software Metrics
Applications of clustering techniques to software partitioning, recovery and restructuring
Journal of Systems and Software - Special issue: Applications of statistics in software engineering
Genetic granular classifiers in modeling software quality
Journal of Systems and Software
Journal of Computer Science and Technology
Segmented software cost estimation models based on fuzzy clustering
Journal of Systems and Software
Analysis of Software Functional Size Databases
Software Process and Product Measurement
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Maximising data retention from the ISBSG repository
EASE'08 Proceedings of the 12th international conference on Evaluation and Assessment in Software Engineering
Probabilistic size proxy for software effort prediction: A framework
Information and Software Technology
International Journal of Intelligent Information Technologies
Information and Software Technology
LMES: A localized multi-estimator model to estimate software development effort
Engineering Applications of Artificial Intelligence
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Parametric software effort estimation models consisting on a single mathematical relationship suffer from poor adjustment and predictive characteristics in cases in which the historical database considered contains data coming from projects of a heterogeneous nature. The segmentation of the input domain according to clusters obtained from the database of historical projects serves as a tool for more realistic models that use several local estimation relationships. Nonetheless, it may be hypothesized that using clustering algorithms without previous consideration of the influence of well-known project attributes misses the opportunity to obtain more realistic segments. In this paper, we describe the results of an empirical study using the ISBSG-8 database and the EM clustering algorithm that studies the influence of the consideration of two process-related attributes as drivers of the clustering process: the use of engineering methodologies and the use of CASE tools. The results provide evidence that such consideration conditions significantly the final model obtained, even though the resulting predictive quality is of a similar magnitude.