AI for Mass-Scale Code Refactoring and Analysis
How to Make AI More Efficient, Cost-Effective, and Accurate at Scale
Book Details
| Authors | Justine Gehring, Olga Kundzich, Pat Johnson |
| Publisher | O'Reilly Media |
| Published | 2024 |
| Edition | 1 |
| Paperback | 43 pages |
| Language | English |
| ISBN-13 | 9781098175849, 9781098175832 |
| ISBN-10 | 1098175840, 1098175832 |
| License | Compliments of Moderne |
Book Description
As the software development landscape evolves, the challenge of managing and refactoring extensive code bases becomes increasingly complex. AI methods of code refactoring, while effective for smaller scales, can falter under the weight of mass-scale operations. The need for efficiency, accuracy, and consistency is more critical than ever.
This key report provides an in-depth exploration of how to optimize AI for these extensive tasks to minimize the need for "human in the loop." Discover how AI can transform the daunting job of mass-scale code refactoring into a streamlined, trustworthy process.
You will:
- Understand the benefits and potentials of multi-repository refactoring supported by AI
- Explore the technologies and techniques that improve AI accuracy for mass-scale refactoring, such as retrieval-augmented generation (RAG)
- Uncover use cases that demonstrate techniques for leveraging AI in handling large code bases
- Learn the critical factors to consider when adopting AI for mass-scale refactoring projects
- Equip yourself to evaluate and choose AI technologies that best fit your needs
This book is published as open-access, which means it is freely available to read, download, and share without restrictions.
If you enjoyed the book and would like to support the author, you can purchase a printed copy (hardcover or paperback) from official retailers.
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