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Total Size:
22.9 MB
Info Hash:
CA9ED2A214A86B3563F96177526B626AB03FA3EA
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Added:
Sept. 30, 2025, 10:56 a.m.
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(Last updated: Sept. 30, 2025, 11:01 a.m.)
| File | Size |
|---|---|
| Wienholt N. GitHub Copilot and AI Coding Tools in Practice...Enterprise 2025.pdf | 22.9 MB |
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22.9 MB
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2025-09-30
| Uploaded by andryold1 | Size 22.9 MB | Health [ 23 /17 ] | Added 2025-09-30 |
NOTE
SOURCE: Wienholt N. GitHub Copilot and AI Coding Tools in Practice...Enterprise 2025
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COVER
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MEDIAINFO
Textbook in PDF format Learn the current state of Generative AI coding tools like GitHub Copilot, what the underlying models mean, and how to use them across the full development life-cycle. Look ahead to the near future of AI-generated software and understand how software developers can stay relevant in the industry. Many companies have predicted that human coders will soon be redundant due to AI-generated code, but there is a big gap between the expectations and what is actually happening on the ground. A closer look at the current state of the tools and research in this area will offer realism and guidance to developers worried regarding redundancy. Close the gap between hype and practical applications by receiving context and clear technical information on usage, understanding, and deployment of these tools. This book addresses the gaps that exist between the flashy demonstrations and effectively using Copilot across the full range of the software development life cycle and covers the big gap that currently exists between agile methodology and successful using of Copilot and coding agents. Copilot can be used effectively in many job roles, from data scientist and DBAs through to devops and architecture, and this book provides detailed coverage of how each of these job functions can take advantage of AI to increase their productivity. The book concludes with an examination on the employment impacts of AI on software engineers, how they can adapt and leverage new skills to combat the threat that AI is believed to present to their job function, and practical ways that engineers can stay relevant in the new AI age. The range of languages in the code bases that the code generation models are trained on is limited. While there are a huge number of projects for front-end work that developers have shared publicly to GitHub (when combined, Type Script and JavaScript represent the top language group), other “boring” languages like SQL have much less training data available. Python, as a more glamorous language, represents 16% of the code pushed to GitHub in Q1 2024, while SQL doesn’t make the top 50 list. What You Will Learn: How to use coding and software AI tools How software AI tools work How software AI tools fit in an industry context How to use AI tools across the SDLC – it's more than just faster coding Who This Book is For: All software industry participants interested in mastering AI-generated code
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