Abstract
Sampling imbalance dataset for software defect prediction using hybrid neuro-fuzzy systems with Naive Bayes classifier
Highlights
The software defect prediction is considered as a crucial activity in the process of decision support in the field of software quality assurance [1÷4].In a view to apply defect prediction schemes in the process of assuring quality software products, several types of machine learning classification algorithms have been adopted for predicting the software defect [5]
The data that is used in this study was provided by the NASA Metrics Data Programa (MDP) repository
Software defect prediction is useful for developers for the identification of defects in accordance with the current software metrics with novel hybrid machine learning Hybrid Neuro-Fuzzy Systems with Naive Bayes technique
Summary
In a view to apply defect prediction schemes in the process of assuring quality software products, several types of machine learning classification algorithms have been adopted for predicting the software defect [5]. As many of the datasets are found to be prone to non-defect type, prediction of software defect using models based on the imbalance characteristics becomes impractical. In the field of machine learning techniques, feature selection plays a key role by involving the learning task that enables the process of predicting datasets possessing high dimensional and noisy attributes. Most feature selection algorithms involve local search rather than the global search throughout the process This is due to the reason that the issues in the feature selection methods are found in the regions ranging from suboptimal and near optimal ends. Finding the solutions in the regions ranging between near-optimal and optimal solutions becomes very difficult by means of feature selection techniques
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