Efficient Dimensionality Reduction using Improved Fuzzy C-Means Entropy Approach with Caps-TripleGAN for Predicting Software Defect in Imbalanced Dataset

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2022 by IJETT Journal
Volume-70 Issue-7
Year of Publication : 2022
Authors : Satya Srinivas Maddipati, Malladi Srinivas
DOI : 10.14445/22315381/IJETT-V70I7P201

How to Cite?

Satya Srinivas Maddipati, Malladi Srinivas, "Efficient Dimensionality Reduction using Improved Fuzzy C-Means Entropy Approach with Caps-TripleGAN for Predicting Software Defect in Imbalanced Dataset," International Journal of Engineering Trends and Technology, vol. 70, no. 7, pp. 1-9, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I7P201

Early detection of bugs or defects in software life cycle reduces the effort required to develop software. Mainly there are two problems in software defect prediction, i.e., dimensionality reduction and class imbalance. Several works are done to predict software defects, but that method does not provide sufficient accuracy, and the error rate increases. To overcome these issues, this work is proposed. In this manuscript, Improved Fuzzy C-means-based Entropy (IFCME) approach with CapsNet triple generative adversarial network (Caps-Triple GAN) for predicting software defect imbalanced dataset for reducing dimensionality and Class Imbalance Problem. Converting the non-linear high dimensional data into low dimensional space to reduce the class imbalance problem uses IFCME. Caps-Triple GANs are used to classify the data with high accuracy and reduce the error rate of software data prediction. The simulation process is executed in the MATLAB platform. The proposed IFCME-Caps-Triple GAN-DR-SDP attains higher accuracy of 23.84%, 32.94%, 36.94%, High Precision of 26.94%, 37.32%, 28.94%, and the proposed method is compared with the existing methods such as Software defect prediction model based on LASSO–SVM (LASSO–SVM-DR-SDP), multi-source heterogeneous cross-project defect prediction via multi-source transfer learning and autoencoder (MHCPDP-MAA-DR-SDP), Tackling class imbalance problem in software defect prediction through cluster-based over-sampling with filtering (KMFOS-DR-SDP)respectively.

Improved Fuzzy C-means-based Entropy (IFCME), Software defect prediction, Caps Net triple generative adversarial network (Caps-Triple GAN), Dimensionality reduction, and Class Imbalance Problem.

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