International Journal of Computer Science & Network Security

국제컴퓨터통신보호논문지학회 (International Journal of Computer Science & Network Security)
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A Hybrid Soft Computing Technique for Software Fault Prediction based on Optimal Feature Extraction and Classification

  • Balaram, A. (Department of CSE, JNTUA University) ;
  • Vasundra, S. (Department of CSE, JNTUA University)
  • 투고 : 2022.05.05
  • 발행 : 2022.05.30
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초록

Software fault prediction is a method to compute fault in the software sections using software properties which helps to evaluate the quality of software in terms of cost and effort. Recently, several software fault detection techniques have been proposed to classifying faulty or non-faulty. However, for such a person, and most studies have shown the power of predictive errors in their own databases, the performance of the software is not consistent. In this paper, we propose a hybrid soft computing technique for SFP based on optimal feature extraction and classification (HST-SFP). First, we introduce the bat induced butterfly optimization (BBO) algorithm for optimal feature selection among multiple features which compute the most optimal features and remove unnecessary features. Second, we develop a layered recurrent neural network (L-RNN) based classifier for predict the software faults based on their features which enhance the detection accuracy. Finally, the proposed HST-SFP technique has the more effectiveness in some sophisticated technical terms that outperform databases of probability of detection, accuracy, probability of false alarms, precision, ROC, F measure and AUC.

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과제정보

Authors thank, Dr.C, Shoba bindu (Director, R&D) JNT University Anantapuramu for providing a assistance to establish working environment in the lab to carry out my present research.

참고문헌

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