AI-Driven Software Development Lifecycle Optimization

AI-Driven Software Development Lifecycle Optimization

Pawan Kumar Goel, Km Komal, Nitish Vashishth
Copyright: © 2024 |Pages: 17
DOI: 10.4018/979-8-3693-3502-4.ch005
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Abstract

This research explores the integration of artificial intelligence (AI) into the software development lifecycle (SDLC) to optimize processes and address evolving industry demands. Against a backdrop of increased complexity in software projects, the study investigates historical context, emphasizing the imperative for AI infusion in SDLC. The primary objective is to illuminate how AI strategically enhances various SDLC stages, elucidating specific challenges it addresses. Employing a multifaceted approach, including literature reviews, case studies, and empirical analyses, the research showcases AI's role in automated code generation, intelligent code reviews, predictive maintenance, AI-powered testing, and dynamic resource allocation. Results demonstrate increased development speed, improved code quality, proactive issue identification, and efficient resource utilization. The chapter synthesizes key insights, underscoring AI's transformative impact on software development efficiency and product quality.
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1. Introduction

In the ever-evolving landscape of software development, the infusion of Artificial Intelligence (AI) into the Software Development Lifecycle (SDLC) has emerged as a transformative strategy to optimize and enhance the entire development process. This chapter aims to delve into the intricacies of this dynamic integration, exploring the historical context, imperative, methods, and results of incorporating AI into SDLC. By addressing the challenges and complexities associated with modern software projects, this research seeks to provide a comprehensive understanding of the profound impact AI can have on the efficiency and quality of software development.

1.2 Background and Context

The field of software development has undergone substantial changes over the years, propelled by the need to meet dynamic industry demands and the increasing complexities associated with modern software projects. Traditional methodologies often faced challenges such as time-consuming manual coding, error-prone processes, and reactive issue identification. As software projects grew in scale and intricacy, the limitations of conventional approaches became evident, necessitating a paradigm shift in how software development is approached.

The historical context of software development reflects a continuous quest for efficiency and quality improvement. The advent of AI marked a pivotal moment, offering the promise of automation, intelligence, and predictive capabilities that could revolutionize the way software is conceptualized, developed, and maintained. From the early days of punch cards to contemporary agile methodologies, the integration of AI into SDLC represents a significant leap forward in the pursuit of optimal software development processes.

1.3 Research Question and Objective

At the core of this research is the exploration of how AI technologies strategically enhance and optimize various stages of the software development lifecycle. The central research question guiding this investigation is: How can Artificial Intelligence be effectively integrated into the Software Development Lifecycle to address challenges, improve efficiency, and elevate the overall quality of software products?

The primary objective is to illuminate the specific challenges within SDLC that AI addresses, fostering a deeper understanding of the transformative potential and the strategic advantages AI brings to the development process. By focusing on practical applications, empirical analyses, and real-world case studies, the research aims to provide actionable insights into the methods through which AI optimizes SDLC.

1.4 Significance and Relevance

The significance of this study lies in its potential to revolutionize the way software development is approached, providing solutions to long-standing challenges and unlocking new possibilities. In an era where technology is advancing rapidly, the integration of AI into SDLC is not merely a choice but a strategic imperative for organizations aiming to stay competitive and deliver cutting-edge software solutions.

The relevance of this research is underscored by the increasing demand for efficient, high-quality software in various industries. As software projects become more intricate, the need for intelligent, automated solutions becomes paramount. By understanding how AI can be harnessed to address specific challenges within SDLC, organizations can make informed decisions about adopting these technologies to improve their development processes.

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