Evaluating the Impact of Computational Intelligence on Software Quality Assurance: Insights from Comparative Analysis of SDLC Models

EasyChair Preprint no. 12853

Abstract

Software Quality Assurance (SQA) plays a critical role in ensuring the reliability, functionality, and performance of software products. With the advent of computational intelligence techniques, such as machine learning and artificial intelligence, there are new opportunities to enhance SQA practices within Software Development Life Cycle (SDLC) models. This research paper conducts a comprehensive analysis to evaluate the impact of computational intelligence on SQA, drawing insights from a comparative analysis of various SDLC models. By examining the integration of computational intelligence techniques into different phases of the SDLC, this paper aims to provide a deeper understanding of how these advancements influence SQA processes. Through literature review, case studies, and comparative analysis, this paper elucidates the benefits, challenges, and future directions of leveraging computational intelligence for SQA in software development.

Keyphrases: Artificial Intelligence, comparative analysis, Computational Intelligence, machine learning, SDLC Models, Software Quality Assurance

@Booklet{EasyChair:12853,
  author = {Kailash Pandey and Wahaj Ahmed},
  title = {Evaluating the Impact of Computational Intelligence on Software Quality Assurance: Insights from Comparative Analysis of SDLC Models},
  howpublished = {EasyChair Preprint no. 12853},

  year = {EasyChair, 2024}}