Enhancing Risk Management in Software Development Through Computational Intelligence: Lessons from Traditional and Emerging SDLC Models

EasyChair Preprint no. 12852

Abstract

Effective risk management is crucial for successful software development projects, ensuring timely delivery of high-quality products within budget constraints. Traditional and emerging Software Development Life Cycle (SDLC) models offer different approaches to risk management, each with its strengths and limitations. This research paper investigates the application of computational intelligence techniques to enhance risk management in software development, drawing lessons from both traditional and emerging SDLC models. Through a comprehensive analysis of existing literature and case studies, this paper explores how machine learning, artificial intelligence, and other computational intelligence methods can be integrated into SDLC processes to identify, assess, and mitigate risks more effectively. The paper also discusses challenges, best practices, and future directions for leveraging computational intelligence in software risk management.

Keyphrases: Artificial Intelligence, Computational Intelligence, machine learning, risk management, SDLC Models, software development

@Booklet{EasyChair:12852,
  author = {Wahaj Ahmed and Anthony Lambert},
  title = {Enhancing Risk Management in Software Development Through Computational Intelligence: Lessons from Traditional and Emerging SDLC Models},
  howpublished = {EasyChair Preprint no. 12852},

  year = {EasyChair, 2024}}