Software engineering metrics and models
Software engineering metrics and models
Function point analysis
Recent advances in software estimation techniques
ICSE '92 Proceedings of the 14th international conference on Software engineering
Estimating Software Project Effort Using Analogies
IEEE Transactions on Software Engineering
Software development cost estimation integrating neural network with cluster analysis
Information and Management
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
An investigation of machine learning based prediction systems
Journal of Systems and Software - Special issue on empirical studies of software development and evolution
Software Metrics: A Rigorous Approach
Software Metrics: A Rigorous Approach
Software Engineering Economics
Software Engineering Economics
An empirical study of process-related attributes in segmented software cost-estimation relationships
Journal of Systems and Software
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
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Parametric software effort estimation models rely on the availability of historical project databases from which estimation models are derived. In the case of large project databases with data coming from heterogeneous sources, a single mathematical model cannot properly capture the diverse nature of the projects under consideration. Clustering algorithms can be used to segment the project database, obtaining several segmented models. In this paper, a new tool is presented, Recursive Clustering Tool, which implements the EM algorithm to cluster the projects, and allows use different regression curves to fit the different segmented models. This different approaches will be compared to each other and with respect to the parametric model that is not segmented. The results allows conclude that depending on the arrangement and characteristics of the given clusters, one regression approach or another must be used,and in general, the segmented model improve the unsegmented one.