Textbook in PDF format

The purpose of this book—Generative AI for Effective Software Development—is to provide a comprehensive, empirically grounded exploration of how generative AI is reshaping the landscape of software development across diverse environments and geographies. This book emphasizes the empirical evaluation of generative AI tools in real-world scenarios, offering insights into their practical efficacy, limitations, and impact on various aspects of software engineering. It focuses on the human aspect, examining how generative AI influences the roles, collaborations, and decision-making processes of developers from different countries and cultures. By presenting case studies, surveys, and interviews from various software development contexts, the book aims to offer a global perspective on the integration of generative AI, highlighting how these advanced tools are adapted to and influence diverse cultural, organizational, and technological environments. This multifaceted approach not only showcases the technological advancements in generative AI but also deeply considers the human element, ensuring that the narrative remains grounded in the practical realities of software developers worldwide. While Generative AI technologies encompass a wide range of data types, our cases focus mainly on LLMs with text and code generation. The evaluation is done with current models, such as Llama 2 or ChatGPT-4, acknowledging the current limitations associated with them.
This era marks a significant advancement and application of generative artificial intelligence (Generative AI). At the time of writing this section, the significance of Generative AI is unquestionable with a prevailing consensus that the technology will bring about a transformative effect across all sectors of society and industry. Generative AI is most referred to as a technology that (i) leverages deep learning models to (ii) generate humanlike content (e.g., images, words) in response to (iii) complex and varied prompts (e.g., languages, instructions, questions).
Generative AI tools are increasingly becoming a staple in software development, aiding in both managerial and technical project facets. Prominent models like Meta’s LLaMA, OpenAI’s ChatGPT, GitHub Copilot, and Amazon CodeWhisperer are reshaping traditional concerns about productivity and quality in technology adoption. These tools, with their advanced code generation capabilities and AI-assisted environments, are transforming the software development process by automating routine tasks, enhancing code, and even writing significant code segments. Beyond coding, generative AI aids in areas like requirements engineering and project management. However, generative AI isn’t just about task automation; it signifies a fundamental shift in problem-solving within software development. This paradigm shift transcends process automation, redefining human roles, teamwork dynamics, and decision-making in software development. Software developers are transitioning from code-centric roles to AI collaborators, focusing on high-level architecture, setting AI goals, and interpreting AI-generated solutions. This shift enables developers to concentrate on more complex, creative aspects, fostering an interdisciplinary approach with teams comprising domain experts, data scientists, and ethicists to maximize generative AI’s potential.
In general, large language models (LLMs) are still easy to produce hallucination, misleading, inconsistent, or unverifiable information. Models built on top of historically biased data pose problems of fairness and trustworthiness, and when incidents happen, issues about safety and responsibility can arise. LLMs may fall short of mastering generation tasks that require domain-specific knowledge or generating structured data. It is nontrivial to inject specialized knowledge into LLMs. Techniques like prompting, augmenting, fine-tuning, and the use of smaller AI models present potential solutions, yet applying them to specific problems is nontrivial. For software engineering tasks, the current evaluation of generative AI focuses more on code generation tasks, with less emphasis on evaluating or researching other tasks such as requirement generation, code fixes, and vulnerability repair. It is anticipated that exploring these areas will be a significant and influential line of research for the software engineering community.
Finally, generative AI will also play a crucial role in democratizing software development. With AI-assisted coding, individuals who may not have formal training in programming will be able to create and modify software. This will break down barriers to entry in the field of software development and foster a more inclusive and diverse community of software creators. It will empower a wider range of people to bring their unique ideas to life, leading to a more vibrant and innovative software landscape. This democratization will not only spur creativity but also lead to the development of solutions that cater to a broader spectrum of needs and challenges in various industries.
Contents:
Part I. Fundamentals of Generative AI
An Overview on Large Language Models
Part II. Patterns and Tools for the Adoption of Generative AI in Software Engineering
Comparing Proficiency of ChatGPT and Bard in Software Development
DAnTE: A Taxonomy for the Automation Degree of Software Engineering Tasks
ChatGPT Prompt Patterns for Improving Code Quality, Refactoring, Requirements Elicitation, and Software Design
Requirements Engineering Using Generative AI: Prompts and Prompting Patterns
Advancing Requirements Engineering Through Generative AI: Assessing the Role of LLMs
Part III. Generative AI in Software Development: Case Studies
Generative AI for Software Development: A Family of Studies on Code Generation
BERTVRepair: On the Adoption of CodeBERT for Automated Vulnerability Code Repair
ChatGPT as a Full-Stack Web Developer
Part IV. Generative AI in Software Engineering Processes
Transforming Software Development with Generative AI: Empirical Insights on Collaboration and Workflow
How Can Generative AI Enhance Software Management? Is It Better Done than Perfect?
Value-Based Adoption of ChatGPT in Agile Software Development: A Survey Study of Nordic Software Experts
Early Results from a Study of GenAI Adoption in a Large Brazilian Company: The Case of Globo
Part V. Future Directions and Education
Generating Explanations for AI-Powered Delay Prediction in Software Projects
Classifying User Intent for Effective Prompt Engineering: A Case of a Chatbot for Startup Teams
Toward Guiding Students: Exploring Effective Approaches for Utilizing AI Tools in Programming Courses