Probabilistic reasoning in expert systems: theory and algorithms
Probabilistic reasoning in expert systems: theory and algorithms
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
Machine Learning Approaches to Estimating Software Development Effort
IEEE Transactions on Software Engineering
Estimating Software Project Effort Using Analogies
IEEE Transactions on Software Engineering
Machine Learning - Special issue on learning with probabilistic representations
Software engineering: theory and practice
Software engineering: theory and practice
An assessment and comparison of common software cost estimation modeling techniques
Proceedings of the 21st international conference 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
Empirical Data Modeling in Software Engineering Using Radial Basis Functions
IEEE Transactions on Software Engineering
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Software Engineering Economics
Software Engineering Economics
Introduction to Bayesian Networks
Introduction to Bayesian Networks
Expert Systems and Probabiistic Network Models
Expert Systems and Probabiistic Network Models
Feature Extraction, Construction and Selection: A Data Mining Perspective
Feature Extraction, Construction and Selection: A Data Mining Perspective
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
Experience With the Accuracy of Software Maintenance Task Effort Prediction Models
IEEE Transactions on Software Engineering
Developing project duration models in software engineering
Journal of Computer Science and Technology
An application of Bayesian network for predicting object-oriented software maintainability
Information and Software Technology
Defect cost flow model: a Bayesian network for predicting defect correction effort
Proceedings of the 6th International Conference on Predictive Models in Software Engineering
Systematic literature review of machine learning based software development effort estimation models
Information and Software Technology
Discretization methods for NBC in effort estimation: an empirical comparison based on ISBSG projects
Proceedings of the ACM-IEEE international symposium on Empirical software engineering and measurement
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The importance of accurate estimation of software development effort is well recognized in software engineering. In recent years, machine learning approaches have been studied as possible alternatives to more traditional software cost estimation methods. The objective of this paper is to investigate the utility of the machine learning algorithm known as the Naive-Bayes classifier for estimating software project effort. We present empirical experiments with the Benchmark 6 data set from the International Software Benchmarking Standards Group to estimate project delivery rates and compare the performance of the Naive-Bayes approach to two other machine learning methods--model trees and neural networks. A project delivery rate is defined as the number of effort hours per function point. The approach described is general and can be used to analyse not only software development data but also data on software maintenance and other types of software engineering. The paper demonstrates that the Naive-Bayes classifier has a potential to be used as an alternative machine learning tool for software development effort estimation.