Semantic Feature Driven Consensus-Based Model for Software Reliability Assessment: A Reusability Sensitive Verification Paradigm

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2022 by IJETT Journal
Volume-70 Issue-4
Year of Publication : 2022
Authors : Prakash V. Parande, M K. Banga
  10.14445/22315381/IJETT-V70I4P209

MLA 

MLA Style: Prakash V. Parande, and M K. Banga "Semantic Feature Driven Consensus-Based Model for Software Reliability Assessment: A Reusability Sensitive Verification Paradigm." International Journal of Engineering Trends and Technology, vol. 70, no. 4, Apr. 2022, pp. 107-121. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I4P209

APA Style: Prakash V. Parande, & M K. Banga. (2022). Semantic Feature Driven Consensus-Based Model for Software Reliability Assessment: A Reusability Sensitive Verification Paradigm. International Journal of Engineering Trends and Technology, 70(4), 107-121. https://doi.org/10.14445/22315381/IJETT-V70I4P209

Abstract
The exponential rise in global competitiveness and quality concern has forced software enterprises to ensure cost-efficiency with uncompromising product reliability. Software developers often intend to use the free-open-source software or class-reuse paradigm to reduce development costs. However, excessive reuse of software components often leads to pre-mature ageing, smells, and malfunction. To alleviate such issues, assessing each class for its reusability can be of great significance. Despite the numerous efforts, the existing approaches have failed to address the problems like class imbalance, shallow feature learning, and, more importantly, low accuracy. In this paper, a robust semantic-feature-driven consensus-based software reusability prediction model is developed for software reliability assessment. To achieve it, at first, it exploits a set of 17 Chidamber and Kamerer OOP matrices obtained by means of WSImport and the CKJM tool. To further enrich intrinsic feature information for future learning, s-Skip Gram-based semantic feature extraction over each metric for every class and SMOTE-ENNresampling algorithm has been employed with variance threshold algorithm and Mann-Whitney significant predictor tests. Min-Max normalization was done on the results to handle issues that rose from convergence and the over-fitting behaviour of classifiers. The simulation results confirmed that the proposed semantic-feature-driven consensus model achieves an accuracy of 98.27%, F-score of 0.983, and AUC of 0.996, which is the highest performance across the existing state-of-art methods.

Keywords
Software Reliability, Reusability Prediction, Consensus Learning, Semantic Features, Software Metrics.

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