LLM-Generated AI Framework for Cloud-Powered Software Development: A Hybrid Fuzzy Integration of WPM, TOPSIS, and Particle Swarm Optimization under the Serverless Revolution
DOI:
https://doi.org/10.15662/IJEETR.2021.0306003Keywords:
Large Language Models (LLMs), Cloud Computing, Serverless Architecture, Software Development, Fuzzy Logic, Weighted Product Model (WPM), TOPSIS, Particle Swarm Optimization (PSO), Hybrid AI Framework, Multi-Criteria Decision-Making (MCDM), Automation, Scalability, Optimization, Intelligent DevOpsAbstract
The emergence of Large Language Models (LLMs) and serverless cloud architectures has redefined the paradigms of intelligent software engineering. This study introduces an LLM-generated AI framework that integrates Fuzzy Weighted Product Model (WPM), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and Particle Swarm Optimization (PSO) to enhance decision intelligence, automation, and scalability in cloud-powered software development. The framework leverages LLMs to autonomously generate, refine, and optimize code and deployment pipelines within serverless environments, while the hybrid fuzzy MCDM–PSO layer dynamically evaluates trade-offs among performance, cost, energy efficiency, and fault tolerance. By combining fuzzy logic for uncertainty handling with PSO’s global optimization capability, the system achieves adaptive orchestration of microservices and AI-driven model components. Experimental simulations on AWS Lambda and Azure Functions environments demonstrate improved deployment efficiency (23–31%), reduced resource consumption (17%), and enhanced accuracy in decision evaluation compared to baseline heuristics. The proposed architecture exemplifies the convergence of LLMs, optimization algorithms, and serverless computing, offering a reproducible pathway toward autonomous, intelligent, and sustainable software engineering in the cloud era.References
1. Chen, S. M., & Cheng, S. H. (2010). Fuzzy multiple attributes group decision-making based on ranking interval type-2 fuzzy sets of linguistic variables. Information Sciences, 180(4), 724–745. https://doi.org/10.1016/j.ins.2009.10.012
2. Sugumar, R. (2016). An effective encryption algorithm for multi-keyword-based top-K retrieval on cloud data. Indian Journal of Science and Technology 9 (48):1-5.
3. Anand, L., & Neelanarayanan, V. (2019). Liver disease classification using deep learning algorithm. BEIESP, 8(12), 5105–5111.
4. Sudhan, S. K. H. H., & Kumar, S. S. (2016). Gallant Use of Cloud by a Novel Framework of Encrypted Biometric Authentication and Multi Level Data Protection. Indian Journal of Science and Technology, 9, 44.
5. Dorigo, M., & Stützle, T. (2004). Ant colony optimization. MIT Press.
6. Eberhart, R. C., & Kennedy, J. (1995). A new optimizer using particle swarm theory. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, 39–43. IEEE.
7. Gonepally, S., Amuda, K. K., Kumbum, P. K., Adari, V. K., & Chunduru, V. K. (2021). The evolution of software maintenance. Journal of Computer Science Applications and Information Technology, 6(1), 1–8. https://doi.org/10.15226/2474-9257/6/1/00150
8. Lin, C. T., & Lee, C. S. G. (1996). Neural fuzzy systems: A neuro-fuzzy synergism to intelligent systems. Prentice Hall.
9. Mishra, A., & Tripathy, A. R. (2016). A comparative study of multi-criteria decision-making methods for software requirement prioritization. International Journal of Computer Applications, 144(9), 1–6.
10. Anand, L., & Neelanarayanan, V. (2019). Feature Selection for Liver Disease using Particle Swarm Optimization Algorithm. International Journal of Recent Technology and Engineering (IJRTE), 8(3), 6434-6439.
11. Sethupathy, U. K. A. (2020). Cloud-powered connected vehicle networks: Enabling smart mobility. World Journal of Advanced Engineering Technology and Sciences, 1(1), 133-147. https://doi.org/10.30574/wjaets.2020.1.1.0021
12. Cherukuri, B. R. (2019). Serverless revolution: Redefining application scalability and cost efficiency. https://d1wqtxts1xzle7.cloudfront.net/121196636/WJARR_2019_0093-libre.pdf?1738736725=&response-content-disposition=inline%3B+filename%3DServerless_revolution_Redefining_applica.pdf&Expires=1762272213&Signature=XCCyVfo54ImYDZxM5lPQQ2nkTOzAKecpW86qlfne0lLpMlvC6WaoSiOBsyS3SyoPj8nAPWdSqFOeiZqIwKsTriCNb6de-mfqXndHQwXRcrA7aVAoQ2txD12Ph36pxjJRJehcVlRK0o878Lh-1nc2mmtJEssNhLC8sVziFBjWuaUiW2Gr0YEZ8ZgIOfHv7gPNREi4JzDmIxpr8eTxb08LoN8KlFSLgouF4SpPoejQYmYOW7JRNijqsMnyhfjSsDv8fdrjSbkb2w-GD7tWhZHVT-1Vu03XPRsjVN-fbMtINmy9tAbgjElqevLlU36g54NdZ8VG4H2pouSeuv55VROnlA__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA
13. Muthirevula, G. R., Kotapati, V. B. R., & Ponnoju, S. C. (2020). Contract Insightor: LLM-Generated Legal Briefs with Clause-Level Risk Scoring. European Journal of Quantum Computing and Intelligent Agents, 4, 1-31.
14. Shi, Y., & Eberhart, R. C. (1998). A modified particle swarm optimizer. Proceedings of the IEEE International Conference on Evolutionary Computation, 69–73. IEEE.
15. Singh, D., & Chana, I. (2015). Cloud resource provisioning: Survey, status and future research directions. Knowledge-Based Systems, 87, 50–69. https://doi.org/10.1016/j.knosys.2015.06.009
16. Chiranjeevi, K. G., Latha, R., & Kumar, S. S. (2016). Enlarge Storing Concept in an Efficient Handoff Allocation during Travel by Time Based Algorithm. Indian Journal of Science and Technology, 9, 40.
17. R. Sugumar, A. Rengarajan and C. Jayakumar, Design a Weight Based Sorting Distortion Algorithm for Privacy Preserving Data Mining, Middle-East Journal of Scientific Research 23 (3): 405-412, 2015.
18. Amuda, K. K., Kumbum, P. K., Adari, V. K., Chunduru, V. K., & Gonepally, S. (2020). Applying design methodology to software development using WPM method. Journal ofComputer Science Applications and Information Technology, 5(1), 1-8..
19. Zhang, Q., Cheng, L., & Boutaba, R. (2010). Cloud computing: State-of-the-art and research challenges. Journal of Internet Services and Applications, 1(1), 7–18.
