The Software Development Life Cycle (SDLC) is essential for producing high-quality software that satisfies the needs and demands of the customer as a whole. We can classify the present Software Development Life Cycles (SDLC) as traditional SDLCs based on the trends, technology, and needs of software products, particularly in the paradigm shift of software engineering. Traditional SDLCs have significant limitations, such as the inability to handle entire projects, a lack of flexibility, and results that are not anticipated at the start of the SDLC. However, because of their distinct qualities, they can be applied to developments that don't call for cutting-edge technology or additional technical elements. The development of software is currently undergoing a paradigm shift that incorporates cutting-edge technologies including software-defined networking, machine learning, the internet of things, and artificial intelligence. The shift brought about by the improvements in software development cannot be adopted by these conventional models. In this study, we plan to develop a revised SDLC model based on AI components. The new model serves as the standard artificial intelligence model for all SDLC phases. This concept will provide a remedy for the problem with conventional SDLCs.

Research Questions

Based on the following research questions this study will propose a novel SDLC model framework.

RQ1: What are the SDLC models proposed already and why they were proposed?

RQ2: What are the paradigm changes has been made or happening in the software developments?

RQ3: What are the problems of existing SDLCs to adapt those paradigm changes?

RQ4: How we can propose a solution for those problems?

Research Objectives

1. Identify the major common drawbacks or issues of traditional SDLCs.
2. Propose a generalized SDLC model to overcome issues of existing SDLCs that adopts new changes in the technologies being adopted.
3. Analyze how the proposed model will incorporate with metaphor paradigm shift in the field of software engineering.
4. Evaluate with proposed model with the emerging technologies.
5. Validation of the proposed model