A Comparative Analysis of Existing Software Development Lifecycle Models and the Proposal of a Novel Model Utilizing Computational Intelligence

EasyChair Preprint no. 12851

Abstract

Software development lifecycle (SDLC) models provide a structured approach to manage the process of software development. Over the years, various SDLC models have been proposed, each with its strengths and weaknesses. This research paper aims to compare and analyze existing SDLC models and propose a novel model that integrates principles of computational intelligence to enhance efficiency, adaptability, and effectiveness in software development processes. Through a comprehensive review of existing models and an exploration of computational intelligence techniques, this paper presents insights into how computational intelligence can be harnessed to address the challenges faced by traditional SDLC models. The proposed model aims to leverage machine learning, optimization algorithms, and other computational intelligence techniques to facilitate decision-making, automate repetitive tasks, and optimize resource allocation throughout the software development lifecycle.

Keyphrases: Agile Methodology, Computational Intelligence, software development lifecycle (SDLC), Software Engineering, Spiral Model, Waterfall model

@Booklet{EasyChair:12851,
  author = {Rahul Agarwal and Wahaj Ahmed},
  title = {A Comparative Analysis of Existing Software Development Lifecycle Models and the Proposal of a Novel Model Utilizing Computational Intelligence},
  howpublished = {EasyChair Preprint no. 12851},

  year = {EasyChair, 2024}}