C4.5: programs for machine learning
C4.5: programs for machine learning
A Unified Framework for Coupling Measurement in Object-Oriented Systems
IEEE Transactions on Software Engineering
Object-oriented metrics: A review of theory and practice
Advances in software engineering
Efficient and Accurate Parallel Genetic Algorithms
Efficient and Accurate Parallel Genetic Algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Introduction to the Theory of Neural Computation
Introduction to the Theory of Neural Computation
A Unified Framework for Cohesion Measurement in Object-OrientedSystems
Empirical Software Engineering
Empirical Software Engineering
A Metrics Suite for Object Oriented Design
IEEE Transactions on Software Engineering
Combining and Adapting Software Quality Predictive Models by Genetic Algorithms
Proceedings of the 17th IEEE international conference on Automated software engineering
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Mining metrics to predict component failures
Proceedings of the 28th international conference on Software engineering
Dimensionality reduction using genetic algorithms
IEEE Transactions on Evolutionary Computation
EvIdentTM: a functional magnetic resonance image analysis system
Artificial Intelligence in Medicine
Proceedings of the Second ACM-IEEE international symposium on Empirical software engineering and measurement
Studying volatility predictors in open source software
Proceedings of the ACM-IEEE international symposium on Empirical software engineering and measurement
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The development of software is a human endeavor and program comprehension is an important factor in software maintenance. Predictive models can be used to identify software components as potentially problematic for the purpose of future maintenance. Such modules could lead to increased development effort, and as such, may be in need of mitigating actions such as refactoring or assigning more experienced developers. Source code metrics can be used as input features to classifiers, however, there exist a large number of structural measures that capture different aspects of coupling, cohesion, inheritance, complexity and size. In machine learning, feature selection is the process of identifying a subset of attributes that improves a classifier's performance. This paper presents initial results when using a genetic algorithm as a method of improving a classifier's ability to discover cognitively complex classes that degrade program understanding.