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...

Full description

Saved in:
Bibliographic Details
Main Authors: Gehring, Justine (Author), Kundzich, Olga (Author), Johnson, Pat (Author)
Format: Electronic eBook
Language:English
Published: Sebastopol, CA O'Reilly Media, Inc. 2024
Edition:First edition.
Subjects:
Links:https://learning.oreilly.com/library/view/-/9781098175849/?ar
Summary: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.
Item Description:Includes bibliographical references
Physical Description:1 Online-Ressource (42 Seiten) illustrations