Novel Fault Prediction Model in Component based Software System for KC1 Dataset
Keywords:
PSO-MVO, Deep Learning, LSTM, Fault Prediction, Feature Selection.Abstract
Present research work is focusing on fault prediction in component-based software system. In order to achieve this objective dataset of KC1 has been considered. This research work outlines a method for establishing reusable software component evaluation criteria. Optimization mechanism named hybrid PSO-MVO mechanism has been applied on component dataset that impact selection. The major objective is to provide smart and optimized solution for component selection. Dataset that is filtered after detecting of optimized value is trained using deep learning model. The accuracy parameters such as recall value, precision, F1 -score would be considered to evaluate the accuracy of optimized component selection model. Such research is supposed to play significant role in area of CBSE by providing high performance and accurate solution. Optimized value has been calculated for Line count of code, Cyclomatic Complexity, Design Complexity, Estimate Time, Difficulty, Intelligence, Efforts. Considering optimized value, the dataset has been filtered to build LSTM based model to detect the faults. Selection of significant attributes and elimination of non-optimized dataset has increased reliability of model.
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