AI for mass-scale code refactoring and analysis how to make AI more efficient, cost-effective, and accurate at scale
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...
| Otros Autores: | , , |
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| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Sebastopol, CA :
O'Reilly Media, Inc
2024.
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| Edición: | First edition |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009849082606719 |
| Sumario: | 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. |
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| Descripción Física: | 1 online resource (42 pages) : illustrations |
| ISBN: | 9781098175849 |
