Generative AI for Effective Software Development

This book is structured to provide a comprehensive understanding of Generative AI and its transformative impact on the field of software engineering. The book is divided into five parts, each focusing on different aspects of Generative AI in software development. As an introduction, Part 1 presents the fundamentals of Generative AI adoption. Part 2 is a collection of empirical studies and delves into the practical aspects of integrating Generative AI tools in software engineering, with a focus on patterns, methodologies, and comparative analyses. Next, Part 3 presents case studies that showcase the application and impact of Generative AI in various software development contexts. Part 4 then examines how Generative AI is reshaping software engineering processes, from collaboration and workflow to management and agile development. Finally, Part 5 looks towards the future, exploring emerging trends, future directions, and the role of education in the context of Generative AI.

The book offers diverse perspectives as it compiles research and experiences from various countries and software development environments. It also offers non-technical discussions about Generative AI in management, teamwork, business and education. This way, it is intended for both researchers in software engineering and for professionals in industry who want to learn about the impact of Generative AI on software development.

Pekka Abrahamsson is a Full Professor of Software Engineering at Tampere University, Finland. He heads the Applied AI Research Centre (AI HUB) and directs GPT-labs, where large language models are developed and tested. He is a pioneer in the field of agile software development and his current research interests focus on advancing software engineering through generative AI technologies. He is a member of the Finnish Academy of Science and Letters and has an h-index of 64.

Foutse Khomh is a Full Professor of Software Engineering at Polytechnique Montréal, a Canada CIFAR AI Chair on Trustworthy Machine Learning Software Systems, an NSERC Arthur B. McDonald Fellow, and an FRQ-IVADO Research Chair on Software Quality Assurance for Machine Learning Applications. He has conducted pioneer work on improving the trustworthiness of artificial intelligence-based software systems and exercised strong leadership in bringing together the software engineering community and the industry, to develop novel theories, techniques, and tools for improving the quality assurance of artificial intelligence-based software systems.