Chapter 1. Introduction to the Red Hat Developer Lightspeed for MTA

Migration Toolkit for Applications (MTA) performs the static code analysis for a specified target technology to which you want to migrate your applications. Red Hat provides 2400+ analysis rules in MTA for various Java technologies and you can extend the ruleset for custom frameworks or new technologies by creating custom rules.

The static code analysis describes the issues in your code that must be resolved. As you perform analysis for a large portfolio of applications, the issue description and the rule definition that may contain additional information form a large corpus of data that contains repetitive patterns of problem definitions and solutions.

Migrators do duplicate work by resolving issues that are repeated across applications in different migration waves.

Red Hat Developer Lightspeed for MTA works by collecting and storing the changes in the code for a large collection of applications, finding context to generate prompts for the LLM of your choice, and by generating code resolutions produced by the LLM to address specific issues.

Red Hat Developer Lightspeed for MTA uses Retrieval Augmented Generation for context-based resolutions of issues in code. By using RAG, Red Hat Developer Lightspeed for MTA improves the context shared with the LLM to generate more accurate suggestions to fix the issue in the code. The context allows the LLM to "reason" and generate suggestions for issues detected in the code. This mechanism helps to overcome the limited context size in LLMs that prevents them from analyzing the entire source code of an application.

The context is a combination of the source code, the issue description, and solved examples:

You can request AI-assisted code resolutions that obtain additional context from several potential sources, such as analysis issues, IDE diagnostic information, and past migration data via the Solution Server.

The Solution Server acts as an institutional memory that stores changes to source codes after analyzing applications in your organization. This helps you to leverage the recurring patterns of solutions for issues that are repeated in many applications.

When you use the Solution Server, Red Hat Developer Lightspeed for MTA suggests a code resolution that is based on solved examples or code changes in past analysis. You can view a diff of the updated portions of the code and the original source code to do a manual review.

It also enables you to control the analysis through manual reviews of the suggested AI resolutions: you can accept, reject or edit the suggested code changes while reducing the overall time and effort required to prepare your application for migration.

In the agentic AI mode, Red Hat Developer Lightspeed for MTA streams an automated analysis of the code in a loop until all issues are resolved and changes the code with the updates. In the initial run, the AI agent:

If you accept that the agentic AI must continue to make changes, it compiles the code and runs a partial analysis. In this iteration, the agentic AI attempts to fix diagnostic issues (if any) generated by tools that you installed in the VS Code IDE. You can review the changes and accept the agentic AI’s suggestion to address these diagnostic issues.

After each iteration of applying changes to the code, the agentic AI asks if you want the agent to continue fixing more issue. When you accept, it runs another iteration of automated analysis until it has resolved all issues or it has made a maximum of two attempts to fix an issue.

Agentic AI generates a new preview in each iteration when it updates the code with the suggested resolutions. The time taken by the agentic AI to complete all iterations depends on the number of new diagnostic issues that are detected in the code.