Robust regression and outlier detection
Robust regression and outlier detection
Software engineering metrics and models
Software engineering metrics and models
Function point analysis
C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning Approaches to Estimating Software Development Effort
IEEE Transactions on Software Engineering
Estimating Software Project Effort Using Analogies
IEEE Transactions on Software Engineering
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Software Engineering Economics
Software Engineering Economics
Machine Learning
An Empirical Study of Analogy-based Software Effort Estimation
Empirical Software Engineering
Data mining and model trees study on GDP and its influence factors
AIASABEBI'11 Proceedings of the 11th WSEAS international conference on Applied informatics and communications, and Proceedings of the 4th WSEAS International conference on Biomedical electronics and biomedical informatics, and Proceedings of the international conference on Computational engineering in systems applications
LMES: A localized multi-estimator model to estimate software development effort
Engineering Applications of Artificial Intelligence
Hi-index | 0.00 |
This paper reports an empirical study that uses clustering techniques to derive segmented models from software engineering repositories, focusing on the improvement of the accuracy of estimates. In particular, we used two datasets obtained from the International Software Benchmarking Standards Group (ISBSG) repository and created clusters using the M5 algorithm. Each cluster is associated with a linear model. We then compare the accuracy of the estimates so generated with the classical multivariate linear regression and least median squares. Results show that there is an improvement in the accuracy of the results when using clustering. Furthermore, these techniques can help us to understand the datasets better; such techniques provide some advantages to project managers while keeping the estimation process within reasonable complexity.