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Research Paper | Computer Science & Engineering | United States of America | Volume 13 Issue 5, May 2024 | Rating: 5.4 / 10
DyGAISP: Generative AI-Powered Approach for Intelligent Software Lifecycle Planning
Aryyama Kumar Jana | Srija Saha | Arnab Dey [9]
Abstract: The paper presents DyGAISP, an innovative framework designed for dynamic generative AI-driven software planning that may be used in many industries. DyGAISP handles the task of reconciling constantly changing requirements, stakeholder preferences, and operational limitations that are inherent in software development. DyGAISP utilizes sophisticated mathematical formulations and state-of-the-art generative AI methods to provide a complete and fast solution for producing optimum software plans. The system starts with thorough preprocessing of incoming data, followed by advanced feature embedding to represent project needs and historical performance data. The generation of dynamic plans is accomplished using a Generative Adversarial Network (GAN), which allows for the generation of plans that can adjust to evolving project dynamics. Following that, optimization methods are used to enhance the produced plans by considering several goals, such as minimizing project completion time, maximizing resource usage, and ensuring stakeholder satisfaction. DyGAISP smoothly incorporates itself into the Software Development Life Cycle (SDLC), offering interfaces that provide effortless integration and interaction with current project management systems. The thorough assessment guarantees the efficiency and reliability of the generated software development plans and evaluating their performance based on predetermined criteria. The potential impact of this approach extends across several sectors, including banking, healthcare, and manufacturing, providing a scalable solution to the challenges faced in modern software projects.
Keywords: Generative artificial intelligence (AI), Software Development Life Cycle (SDLC), Dynamic Optimization, Software Planning, Stakeholder management, Resource Utilization, Generative Adversarial Network (GAN), Adaptability
Edition: Volume 13 Issue 5, May 2024,
Pages: 1275 - 1282