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NOTE
SOURCE: Laster B. The AI-Enabled SDLC. A Tech Leader's Guide...2025
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COVER
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MEDIAINFO
Textbook in PDF format SDLC с поддержкой искусственного интеллекта: руководство для технических лидеров по пути в будущее разработки программного обеспечения Like CI/CD, vulnerability remediation, and cloud adoption before it, the use of Generative AI (GenAI) is no longer a research project or recommended practice. It's a necessity and a competitive advantage. GenAI has the potential to help your engineering team create and deliver more efficiently and smoothly, with higher quality and less cognitive load, than ever before. But in order to realize these potential benefits, tech leaders need to know how to apply it effectively throughout the phases of your Software Development Life Cycle (SDLC). The Software Development Life Cycle is the cost-effective and time-efficient process that development teams use to design and build high-quality software. The goal of SDLC is to minimize project risks through forward planning so that software meets customer expectations during production and beyond. This methodology outlines a series of steps that divide the software development process into tasks you can assign, complete, and measure. In short, the SDLC is about how you produce software, and do it well, in an orderly, collaborative, and transparent way. Ideally, it’s also cost effective, timely, and high quality. (If not, though, incorporating AI isn’t a cure-all, although it has the potential to help.) Within the SDLC, there are different options for strategies to govern how work flows at the high level. These strategies have names you’re likely well-acquainted with, or at least recognize, such as Agile, Waterfall, Spiral, and others. Regardless of which strategy or methodology you’re using, there’s an iterative set of phases involved. And like the definition of SDLC itself, there’s no universal, standard set. The ones you use may be significantly different from, or significantly similar to, what another company, or even another organization in your company, is using. Intended Audience The primary audience for this book is the engineering team lead, driving software production, who wants to understand how to incorporate GenAI tooling effectively and efficiently in their SDLC. In terms of background, we expect that the reader is someone who has significant experience in designing and writing code, as well as the mechanisms and best practices for getting it to production, such as CI/CD. The assumption is that you also have those in place and working well. On the AI side, you don’t need to be an AI expert to read this. We’ll be focusing on GenAI and the tools that help with coding specifically. Therefore, some basic familiarity with the concepts of large language models (LLMs), chat interfaces, and code completion is assumed, but we’ll also briefly survey LLMs in Chapter 1. Beyond that target audience, we think that anyone involved with software development, from developers to the chief technical officer (CTO), can find value in the chapters. If there are particular parts that strike you as potentially useful for others in different roles, we encourage you to share with them. Even if not directly applicable in their position, it may be the catalyst for a useful discussion. Preface “Generative AI in Software Development” “Opportunities and Challenges: What to Expect from Adding Generative AI to Your Process” “Planning with AI” “Using AI for Code Creation and New Feature Development” “Increasing Test Coverage Through AI Generation” “Resolving Bugs with AI” “Automated Creation of Documentation” “Simplifying Maintenance with AI Onboarding and Explanations” “Augmenting, Refactoring, and Updating with AI” “From AI Assistant to AI Engineer: The Future of AI in Software Development”
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