Hybrid optimization driven ridenn for software reusability estimation
Joint Authors
Source
Journal of Engineering Research
Issue
Vol. 8, Issue 4 (31 Dec. 2020), pp.99-116, 18 p.
Publisher
Kuwait University Academic Publication Council
Publication Date
2020-12-31
Country of Publication
Kuwait
No. of Pages
18
Main Subjects
Information Technology and Computer Science
Abstract EN
Measuring the software reusability has become a prime concern in maintaining the quality of the software.
Several techniques use software related metrics and measure the reusability factor of the software, but still face a lot of challenges.
This work develops the software reusability estimation model for efficiently measuring the quality of the software components over time.
Here, the Rider based Neural Network has been used along with the hybrid optimization algorithm for defining the reusability factor.
Initially, nine software related metrics are extracted from the software.
Then, a holoentropy based log function identifies the Measuring the software reusability has become a prime concern in maintaining the quality of the software.
Several techniques use software related metrics and measure the reusability factor of the software, but still face a lot of challenges.
This work develops the software reusability estimation model for efficiently measuring the quality of the software components over time.
Here, the Rider based Neural Network has been used along with the hybrid optimization algorithm for defining the reusability factor.
Initially, nine software related metrics are extracted from the software.
Then, a holoentropy based log function identifies the normalized metric function and provides it to the proposed Cat Swarm Rider Optimization based Neural Network (C-RideNN) algorithm for the software reusability estimation.
The proposed C-RideNN algorithm uses the existing Cat Swarm Optimization (CSO) along with the Rider Neural Network (RideNN) for the training purpose.
Experimentation results of the proposed C-RideNN are evaluated based on metrics, such as Magnitude of Absolute Error (MAE), Mean Magnitude of the Relative Error (MMRE), and Standard Error of the Mean (SEM).
The simulation results reveal that the proposed C-RideNN algorithm has improved performance with 0.0570 as MAE, 0.0145 as MMRE, and 0.6133 as SEM.
American Psychological Association (APA)
Vankudoth, Ramu& P. Shireesha. 2020. Hybrid optimization driven ridenn for software reusability estimation. Journal of Engineering Research،Vol. 8, no. 4, pp.99-116.
https://search.emarefa.net/detail/BIM-1494673
Modern Language Association (MLA)
Vankudoth, Ramu& P. Shireesha. Hybrid optimization driven ridenn for software reusability estimation. Journal of Engineering Research Vol. 8, no. 4 (Dec. 2020), pp.99-116.
https://search.emarefa.net/detail/BIM-1494673
American Medical Association (AMA)
Vankudoth, Ramu& P. Shireesha. Hybrid optimization driven ridenn for software reusability estimation. Journal of Engineering Research. 2020. Vol. 8, no. 4, pp.99-116.
https://search.emarefa.net/detail/BIM-1494673
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 115-116
Record ID
BIM-1494673