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Automated code remediation at scale : the rose of AI in application modernization and security / Pat Johnson, Olga Kundzich, Jonathan Schneider.
- Format:
- Book
- Author/Creator:
- Johnson, Pat, author.
- Kundzich, Olga, author.
- Schneider, Jonathan (Jonathan K.), author.
- Language:
- English
- Subjects (All):
- Computer programming.
- Computer networks--Security measures.
- Computer networks.
- Physical Description:
- 1 online resource (50 pages)
- Edition:
- Second edition.
- Place of Publication:
- Sebastopol, CA : O'Reilly Media, Inc., [2025]
- Summary:
- The software industrial revolution has arrived. Developers are building software faster than ever--assembled from third-party components, open source libraries, and AI-generated code. But what about maintaining and securing all of the code built by humans and AI? And how should companies address the corresponding acceleration of technical debt? Automated Code Remediation at Scale explains how organizations can automate software modernization and security for large, complex codebases. Packed with new content on the role AI tools now play in auto-remediation, this expanded second edition provides the guidance and real-world insights that will help you stay on top of software vulnerabilities and maintenance and tackle technical debt head-on. Understand the complexity of today's applications and the challenges of managing software vulnerabilities and maintenance (even with the help of AI assistants) Discover why automated code remediation is a necessity for modern large-scale codebases and get best practices for adoption Explore the characteristics required of the technology driving automated code remediation, including the roles of AI and rules-based code automation Learn from real-world case studies what leads organizations to automate code remediation and how it impacts the way they work Apply strategies for communicating about and approaching vulnerability management and framework migrations with automated, AI-driven code remediation.
- Notes:
- OCLC-licensed vendor bibliographic record.
- ISBN:
- 979-83-416-2763-5
- OCLC:
- 1519497666
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