Particle Swarm Optimization Based Hyper Integral Approach for Enhancing Software Quality
DOI:
https://doi.org/10.22399/ijcesen.814Keywords:
Software Quality, Particle Swarm Optimization, Software Development Cycles, Software Inspections, Faults and Failures, Hyper Integral ApproachAbstract
Innovation and competitiveness in the software engineering sector have been booming recently. In order to stay in business, software companies need to provide affordable, high-quality software solutions on schedule. A crucial question is whether it is possible to obtain high-quality software products without negatively affecting development effort and cycle time for software developers. Longer cycle times and more development effort are the only ways to deploy software techniques to increase software quality, according to conventional ideas. Another school of thought holds that the understanding aging leader method, which is a Particle Swarm Optimization (PSO) technique, can simultaneously increase software quality, speed up software development cycles, and reduce developers' effort. A software program defect or bug occurs when a software system fails to meet a functional requirement as stated in the standard specifications or as per the acceptable end-user requirements, even if those requirements are not explicitly mentioned. Integrating quality assurance procedures into every step of the software development lifecycle is the main focus of the Hyper Integral Approach to software quality. By bridging the gap between development and quality assurance, this methodology hopes to boost collaboration, guarantee continuous testing, and raise software quality generally. This research proposes a Hyper Integral Approach (HIA) using Particle Swarm Optimization for enhancing software quality (HIA-PSO-ESQ). The proposed model provides a quality software in less time when contrasted to traditional methods.
References
M. R. Belgaum et al., (2023). Self-Socio Adaptive Reliable Particle Swarm Optimization Load Balancing in Software-Defined Networking, IEEE Access, 11,101666-101677, doi: 10.1109/ACCESS.2023.3314791.
Y. S. Baguda, (2020). Energy-Efficient Biocooperative Video-Aware QoS-Based Multiobjective Cross-Layer Optimization for Wireless Networks, IEEE Access, 8,127034-127047, doi: 10.1109/ACCESS.2020.3008257.
E. Guler, (2024) CITE-PSO: Cross-ISP Traffic Engineering Enhanced by Particle Swarm Optimization in Blockchain Enabled SDONs, IEEE Access, 12,27611-27632, doi: 10.1109/ACCESS.2024.3367600.
H. Das et al., (2024). Enhancing Software Fault Prediction Through Feature Selection With Spider Wasp Optimization Algorithm, IEEE Access, 12,105309-105325, doi: 10.1109/ACCESS.2024.3435333.
D. K. Jain, A. Kumar, S. R. Sangwan, G. N. Nguyen and P. Tiwari, (2019).A Particle Swarm Optimized Learning Model of Fault Classification in Web-Apps, IEEE Access, 7,18480-18489, doi: 10.1109/ACCESS.2019.2894871.
H. -E. Tseng, C. -C. Chang and T. -W. Chung, (2022). Applying Improved Particle Swarm Optimization to Asynchronous Parallel Disassembly Planning, IEEE Access, 10,80555-80564, doi: 10.1109/ACCESS.2022.3195863.
W. Li, Y. Chen, Q. Cai, C. Wang, Y. Huang and S. Mahmoodi, (2022). Dual-Stage Hybrid Learning Particle Swarm Optimization Algorithm for Global Optimization Problems, Complex System Modeling and Simulation, 2(4),288-306, doi: 10.23919/CSMS.2022.0018.
D. Dabhi and K. Pandya, (2020) Uncertain Scenario Based MicroGrid Optimization via Hybrid Levy Particle Swarm Variable Neighborhood Search Optimization (HL_PS_VNSO), IEEE Access, 8,108782-108797, doi: 10.1109/ACCESS.2020.2999935.
H. A. Mahmoud, A. Imran, C. Anwar Ul Hassan and M. A. El-Meligy, (2024). Optimizing Accounting Information Systems With Hybrid Capsule Network and Honey Badger Particle Swarm Optimization, IEEE Access, 12,153346-153359, doi: 10.1109/ACCESS.2024.3481034.
L. Yang, Z. Li, D. Wang, H. Miao and Z. Wang, (2021). Software Defects Prediction Based on Hybrid Particle Swarm Optimization and Sparrow Search Algorithm, IEEE Access, 9,60865-60879, doi: 10.1109/ACCESS.2021.3072993.
E. Selvamanju, & V. Baby Shalini. (2024). 5G Network needs estimation & Deployment Plan Using Geospatial Analysis for efficient data usage, Revenue Generation. International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.692
ECER, B., & AKTAŞ, A. (2019). Clustering of European Countries in terms of Healthcare Indicators. International Journal of Computational and Experimental Science and Engineering, 5(1), 23–26. Retrieved from https://www.ijcesen.com/index.php/ijcesen/article/view/80
AYAN, O., DEMİREZ, D. Z., KİZİLOZ, H. K., INCİ, G., ISLEYEN, S., & ERGİN, S. (2018). The Detection of Spoiled Fruits on a Conveyor Belt Using Image Processing Techniques and OPC Server Software. International Journal of Computational and Experimental Science and Engineering, 4(1), 11–15. Retrieved from https://www.ijcesen.com/index.php/ijcesen/article/view/57
AY, S. (2024). The Use of Agile Models in Software Engineering: Emerging and Declining Themes. International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.703
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