Название: Automated Code Remediation at Scale: How to Refactor and Secure the Modern Software Supply Chain, 2nd Edition
Автор: Pat Johnson, Olga Kundzich, Jonathan Schneider
Издательство: O’Reilly Media, Inc.
Год: 2025-05-05
Язык: английский
Формат: pdf, epub
Размер: 10.1 MB

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.

Development teams have relied on code remediation and refactoring to chip away at technical debt—whether it’s fixing security vulnerabilities, migrating frameworks, updating dependencies, or improving code quality. This is tedious and error-prone work that requires line-by-line, repository-by-repository changes. It consumes valuable time that could be spent on business-critical initiatives, which means this work is often deferred.

Now, let’s imagine a world where code remediation is automated across your entire codebase. It’s a world where eliminating technical debt is no longer a time-consuming chore but a continuous, automated process. Your teams could address vulnerabilities more quickly and holistically, and developers could focus on the work that truly drives business value. Let’s find out how you get there from here. In this chapter, we’ll explore the growing complexity of modern software, emerging trends in managing technical debt—and why mass-scale automated code remediation is required.

There are many building-block recipes, such as find method, change method, find transitive dependency, upgrade dependency, and exclude dependency. These recipes in turn can be composed into more complex recipes by grouping them into a composite recipe. When the building blocks are not enough, a recipe can be written as a program in the same language as the code we want to transform—encapsulating complex logic with the full expressiveness of the language already familiar to developers.

These building blocks abstract many of the details to ensure that edits that we make to source code obey the original style of the project. Recipe authors focus on transforming code, and the refactoring engine tracks the style of projects and application of recipes to codebases in a style-preserving manner. To understand the power of recipes, let’s look at a major version framework migration in the Java ecosystem: upgrading to Spring Boot 3.4 from Spring Boot 2.3 and Java 8. You have a lot of work ahead:

• Spring Boot 3.x migration requires at minimum Java 17.
• Java 17 requires first migrating to Java 11 and also includes migration to Jakarta EE 9.
• You also have to migrate from Spring Boot 2.3 to 2.4, which requires updating JUnit 4 to 5, which requires updating Mockito 1 to 3.

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






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