Optimizing Resource Allocation in Software Projects: a Comparative Analysis of SDLC Models with Computational Intelligence Integration

EasyChair Preprint 12855

Abstract

Efficient resource allocation is essential for the successful completion of software projects, ensuring that tasks are assigned to the right people at the right time to meet project objectives within schedule and budget constraints. Traditional Software Development Life Cycle (SDLC) models offer predefined processes for resource allocation, but they may not fully address the dynamic nature of modern software development projects. This research paper presents a comparative analysis of different SDLC models with computational intelligence integration to optimize resource allocation in software projects. By leveraging machine learning, artificial intelligence, and other computational intelligence techniques, organizations can improve resource utilization, enhance project efficiency, and mitigate risks. Through a comprehensive examination of existing literature, case studies, and practical insights, this paper explores the benefits, challenges, and best practices of integrating computational intelligence into resource allocation processes across various SDLC models.

Keyphrases: Artificial Intelligence, Computational Intelligence, SDLC Models, machine learning, resource allocation, software projects

@booklet{EasyChair:12855,
  author    = {Suniti Purohit and Wahaj Ahmed},
  title     = {Optimizing Resource Allocation in Software Projects: a Comparative Analysis of SDLC Models with Computational Intelligence Integration},
  howpublished = {EasyChair Preprint 12855},
  year      = {EasyChair, 2024}}