Software engineering metrics and models
Software engineering metrics and models
A Pattern Recognition Approach for Software Engineering Data Analysis
IEEE Transactions on Software Engineering - Special issue on software measurement principles, techniques, and environments
C4.5: programs for machine learning
C4.5: programs for machine learning
Robust regression for developing software estimation models
Journal of Systems and Software
Machine Learning Approaches to Estimating Software Development Effort
IEEE Transactions 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
Software Engineering Economics
Software Engineering Economics
Software development cost estimation approaches – A survey
Annals of Software Engineering
A Simulation Tool for Efficient Analogy Based Cost Estimation
Empirical Software Engineering
A Further Empirical Investigation of the Relationship Between MRE and Project Size
Empirical Software Engineering
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Neural Network Approach for Software Cost Estimation
ITCC '05 Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'05) - Volume II - Volume 02
Normalization as a Preprocessing Engine for Data Mining and the Approach of Preference Matrix
DEPCOS-RELCOMEX '06 Proceedings of the International Conference on Dependability of Computer Systems
Journal of Computer Science and Technology
A PSO-based model to increase the accuracy of software development effort estimation
Software Quality Control
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Software cost/effort estimation is still an open challenge. Many researchers have proposed various methods that usually focus on point estimates. Until today, software cost estimation has been treated as a regression problem. However, in order to prevent overestimates and underestimates, it is more practical to predict the interval of estimations instead of the exact values. In this paper, we propose an approach that converts cost estimation into a classification problem and that classifies new software projects in one of the effort classes, each of which corresponds to an effort interval. Our approach integrates cluster analysis with classification methods. Cluster analysis is used to determine effort intervals while different classification algorithms are used to find corresponding effort classes. The proposed approach is applied to seven public datasets. Our experimental results show that the hit rate obtained for effort estimation are around 90---100%, which is much higher than that obtained by related studies. Furthermore, in terms of point estimation, our results are comparable to those in the literature although a simple mean/median is used for estimation. Finally, the dynamic generation of effort intervals is the most distinctive part of our study, and it results in time and effort gain for project managers through the removal of human intervention.