Neurocomputing
Introduction to the theory of neural computation
Introduction to the theory of neural computation
Fundamentals of neural networks: architectures, algorithms, and applications
Fundamentals of neural networks: architectures, algorithms, and applications
Selected papers of the sixth annual Oregon workshop on Software metrics
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
An introduction to genetic algorithms
An introduction to genetic algorithms
Software metrics (2nd ed.): a rigorous and practical approach
Software metrics (2nd ed.): a rigorous and practical approach
A predictive metric based on discriminant statistical analysis
ICSE '97 Proceedings of the 19th international conference on Software engineering
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Designing Neural Networks using Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Using the genetic algorithm to build optimal neural networks for fault-prone module detection
ISSRE '96 Proceedings of the The Seventh International Symposium on Software Reliability Engineering
Training feedforward neural networks using genetic algorithms
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Comments on "The Confounding Effect of Class Size on the Validity of Object-Oriented Metrics"
IEEE Transactions on Software Engineering
Assessment of a New Three-Group Software Quality Classification Technique: An Empirical Case Study
Empirical Software Engineering
On-line prediction of software reliability using an evolutionary connectionist model
Journal of Systems and Software
Object-oriented software fault prediction using neural networks
Information and Software Technology
Evolutionary software engineering, a review
Applied Soft Computing
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In this empirical study, from a large data set of software metrics for program modules, thirty distinct partitions into training and validation sets are automatically generated with approximately equal distributions of fault-prone and not-fault-prone modules. Thirty classification models are built for each of the two approaches considered -- discriminant analysis and the evolutionary neural network (ENN) approach -- and their performances on corresponding data sets are compared. The lower error proportions for ENNs on fault-prone, not-fault-prone, and overall classification were found to be statistically significant. The robustness of ENNs follows from their superior performance on the range of data configurations used. It is suggested that ENNs can be effective in other software reliability problem domains, where they have been largely ignored.