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A Nonlinear Weight-Optimized Maintainability Index of Software Metrics by Grey Wolf Optimization

A Nonlinear Weight-Optimized Maintainability Index of Software Metrics by Grey Wolf Optimization

Gokul Yenduri, Veeranjaneyulu Naralasetti
Copyright: © 2021 |Volume: 12 |Issue: 2 |Pages: 21
ISSN: 1947-9263|EISSN: 1947-9271|EISBN13: 9781799861331|DOI: 10.4018/IJSIR.2021040101
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MLA

Yenduri, Gokul, and Veeranjaneyulu Naralasetti. "A Nonlinear Weight-Optimized Maintainability Index of Software Metrics by Grey Wolf Optimization." IJSIR vol.12, no.2 2021: pp.1-21. http://doi.org/10.4018/IJSIR.2021040101

APA

Yenduri, G. & Naralasetti, V. (2021). A Nonlinear Weight-Optimized Maintainability Index of Software Metrics by Grey Wolf Optimization. International Journal of Swarm Intelligence Research (IJSIR), 12(2), 1-21. http://doi.org/10.4018/IJSIR.2021040101

Chicago

Yenduri, Gokul, and Veeranjaneyulu Naralasetti. "A Nonlinear Weight-Optimized Maintainability Index of Software Metrics by Grey Wolf Optimization," International Journal of Swarm Intelligence Research (IJSIR) 12, no.2: 1-21. http://doi.org/10.4018/IJSIR.2021040101

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Abstract

Maintainability index (MI) is a software metric that offers measurements of the maintainability before release of the software by facilitating several substantial features of the system. In general, there is a common formula for determining the MI for all the software metrics to ensure the system's reliability. As it does not provide appropriate results regarding the reliability of the system, it is essential to focus on the next level of MI of software. Hence, this paper intends to allot an optimal weight and a constant to each software metric, which is optimized by grey wolf optimization (GWO). As a result, it can provide a new variant of MI by proposed enhanced model-GWO (EM-GWO). This optimized MI can ensure the efficiency of the respective software in such a way that it can provide an enhanced score from the system. Further, the proposed method is compared with conventional models such as enhanced model-generic algorithm (EM-GA), EM-particle swarm optimization (PSO), EM-ant bee colony (ABC), EM-differential evolution (DE), and EM-fire fly (FF), and the results are obtained.

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