Author Identifier

Iqbal H. Sarker: https://orcid.org/0000-0003-1740-5517

Document Type

Journal Article

Publication Title

IEEE Access

Publisher

IEEE

School

Centre for Securing Digital Futures / School of Science

RAS ID

77090

Comments

Faruqui, N., Thatoi, P., Choudhary, R., Roncevic, I., Alqahtani, H., Sarker, I. H., & Khanam, S. (2024). AI-analyst: An AI-assisted SDLC analysis framework for business cost optimization. IEEE Access, 12, 195188-195203 https://doi.org/10.1109/ACCESS.2024.3519423

Abstract

Managing the System Development Lifecycle (SDLC) is a complex task because of its involvement in coordinating diverse activities, stakeholders, and resources while ensuring project goals are met efficiently. The complex nature of the SDLC process leaves plenty of scope for human error, which impacts the overall business cost. This paper introduces AI-Analyst, an AI-assisted framework developed using the transformer-based model with more than 150 million parameters to assist with SDLC management. It minimizes manual effort errors, optimizes resource allocation, and improves decision-making processes, resulting in substantial cost savings. The statistical analysis shows that it saves around 53.33% of costs in an experimental project. The transformer model has been trained with a uniquely prepared dataset tailored for SDLC through transfer learning. It achieved impressive results, with an accuracy of 91.5%, precision of 91.9%, recall of 91.3%, and an F1-score of 91.5%, demonstrating its high reliability and performance. The perplexity score of 15 further indicates the model's strong language understanding capabilities to retrieve relations from complex characteristics of Natural Language Processing (NLP). The AI-Analyst framework represents a significant advancement in integrating Large Language Models (LLMs) into SDLC, offering a scalable and cost-effective solution for optimizing business processes.

DOI

10.1109/ACCESS.2024.3519423

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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