MTA-5378 Developer Lightspeed Introduction #172
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| [id="intro-to-the-cli_{context}"] | ||
| = Introduction to the {ProductShortName} Developer Lightspeed | ||
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| Starting from 8.0.0, you can use {ProductFullName} Developer Lightspeed for application modernization in your organization by running Artificial Intelligence-driven static code analysis for Java applications. Developer Lightspeed gains context for an analysis from historical changes to source code through previous analysis (called solved examples) and the description of issues available in both default and custom rule sets. Thus, when you deploy Developer Lightspeed for analyzing your entire application portfolio, it enables you to be consistent with the common fixes you need to make in the source code of any Java application. It also enables you to control the analysis through manual reviews of the suggested AI fixes by accepting or rejecting the changes, while reducing the overall time and effort required to prepare your application for migration. |
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| Starting from 8.0.0, you can use {ProductFullName} Developer Lightspeed for application modernization in your organization by running Artificial Intelligence-driven static code analysis for Java applications. Developer Lightspeed gains context for an analysis from historical changes to source code through previous analysis (called solved examples) and the description of issues available in both default and custom rule sets. Thus, when you deploy Developer Lightspeed for analyzing your entire application portfolio, it enables you to be consistent with the common fixes you need to make in the source code of any Java application. It also enables you to control the analysis through manual reviews of the suggested AI fixes by accepting or rejecting the changes, while reducing the overall time and effort required to prepare your application for migration. | |
| Starting from 8.0.0, you can use {ProductFullName} Developer Lightspeed for application modernization in your organization by running Artificial Intelligence-driven static code analysis for Java applications. Developer Lightspeed gains context for an analysis from historical changes to source code through previous analysis (called solved examples) and the description of issues available in both default and custom rule sets. Thus, when you deploy Developer Lightspeed for analyzing your entire application portfolio, it enables you to be consistent with the common fixes you need to make in the source code of any Java application. It also enables you to control the analysis through manual reviews of the suggested AI fixes by accepting or rejecting the changes while reducing the overall time and effort required to prepare your application for migration. |
anarnold97
reviewed
Jul 31, 2025
anarnold97
reviewed
Jul 31, 2025
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| == How does Developer Lightspeed work | ||
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| The main components of Developer Lightspeed are the large language model (LLM), a visual studio code (VS Code) extension, and the solution server. |
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| The main components of Developer Lightspeed are the large language model (LLM), a visual studio code (VS Code) extension, and the solution server. | |
| The main components of Developer Lightspeed are the large language model (LLM), a Visual Studio Code (VS Code) extension, and the solution server. |
anarnold97
reviewed
Jul 31, 2025
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| When you initiate an analysis, Developer Lightspeed creates a context to generate a hint or a prompt that is shared with your LLM. The context is drawn from profile configuration that contains the target technology or the source technology that you configure for migration. Based on the source or target configuration, Developer Lightspeed checks the associated rule set containing rules that describe what needs to be fixed as the first input for the LLM prompt. As the second input, Developer Lightspeed uses the solved examples that are stored in the solution server from previous analyses in which the source code changed after applying the fixes. | ||
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| The solution server uses Model Context Protocol (MCP) to act as an institutional memory that stores changes to source codes from analyzing all the applications in your organization. This helps you to leverage the recurring patterns of solutions for issues that are repeated in many applications. Solution server reuses these past solutions through fix suggestions in later migrations, leading to faster, more reliable code changes as you migrate applications in different migration waves. At the organizational level, the solution server can also help Developer Lightspeed tackle a new issue given how a similar issue was resolved in a previous analysis. |
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| The solution server uses Model Context Protocol (MCP) to act as an institutional memory that stores changes to source codes from analyzing all the applications in your organization. This helps you to leverage the recurring patterns of solutions for issues that are repeated in many applications. Solution server reuses these past solutions through fix suggestions in later migrations, leading to faster, more reliable code changes as you migrate applications in different migration waves. At the organizational level, the solution server can also help Developer Lightspeed tackle a new issue given how a similar issue was resolved in a previous analysis. | |
| The solution server uses Model Context Protocol (MCP) to act as an institutional memory that stores changes to source codes from analyzing all the applications in your organization. This helps you to leverage the recurring patterns of solutions for issues that are repeated in many applications. Solution Server reuses these past solutions through fix suggestions in later migrations, leading to faster, more reliable code changes as you migrate applications in different migration waves. At the organizational level, the solution server can also help Developer Lightspeed tackle a new issue given how a similar issue was resolved in a previous analysis. |
anarnold97
reviewed
Jul 31, 2025
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| * Plans the context to define the issues. | ||
| * Chooses a suitable sub agent for the analysis task. | ||
| Works with the LLM-to generate fix suggestions. The reasoning transcript and files to be changed are displayed to the user. |
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| Works with the LLM-to generate fix suggestions. The reasoning transcript and files to be changed are displayed to the user. | |
| Works with the LLM to generate fix suggestions. The reasoning transcript and files to be changed are displayed to the user. |
anarnold97
reviewed
Jul 31, 2025
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| The hint or the prompt generated by Developer Lightspeed is the well-defined context for identifying issues that allows the LLM to "reason" and generate the fix suggestions. This mechanism helps to overcome the limited context size in LLMs that prevents them from analyzing the entire source code of an application. You can review the suggested change and accept or reject the update to the code per issue or for all the issues. | ||
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| Developer Lightspeed supports different goals of analysis through the three modes: the agentic AI, the RAG solution delivered by the solution server, and the demo mode. |
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| Developer Lightspeed supports different goals of analysis through the three modes: the agentic AI, the RAG solution delivered by the solution server, and the demo mode. | |
| Developer Lightspeed supports different goals of analysis through the three modes: the Agentic AI, the Retrieval-Augmented Generation (RAG) solution delivered by the solution server, and the demo mode. |
anarnold97
reviewed
Jul 31, 2025
| Works with the LLM-to generate fix suggestions. The reasoning transcript and files to be changed are displayed to the user. | ||
| * Applies the changes to the code once the user approves the updates. | ||
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| If you accept that the agentic AI must continue to make changes, it compiles the code and runs a partial analysis. In this phase, the agentic AI can detect diagnostic issues (if any) generated by tools that you installed in the VS Code IDE. You can accept the agentic AI's suggestion to fix these diagnostic issues too. After every phase of applying changes to the code, the agentic AI runs another round of automated analysis depending on your acceptance, until it has run through all the files in your project and fixed the issues in the code. Agentic AI generates a new file in each round when it applies the fix suggestions in the code. The time taken by the agentic AI to complete several rounds of analysis depends on the size of the application, the number of issues, and the complexity of the code. |
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| If you accept that the agentic AI must continue to make changes, it compiles the code and runs a partial analysis. In this phase, the agentic AI can detect diagnostic issues (if any) generated by tools that you installed in the VS Code IDE. You can accept the agentic AI's suggestion to fix these diagnostic issues too. After every phase of applying changes to the code, the agentic AI runs another round of automated analysis depending on your acceptance, until it has run through all the files in your project and fixed the issues in the code. Agentic AI generates a new file in each round when it applies the fix suggestions in the code. The time taken by the agentic AI to complete several rounds of analysis depends on the size of the application, the number of issues, and the complexity of the code. | |
| If you accept that the agentic AI must continue to make changes, it compiles the code and runs a partial analysis. In this phase, the agentic AI can detect diagnostic issues (if any) generated by tools that you installed in the VS Code IDE. You can accept the agentic AI's suggestion to address these diagnostic issues too. After every phase of applying changes to the code, the agentic AI runs another round of automated analysis depending on your acceptance, until it has run through all the files in your project and resolved the issues in the code. Agentic AI generates a new file in each round when it applies the fix suggestions in the code. The time taken by the agentic AI to complete several rounds of analysis depends on the size of the application, the number of issues, and the complexity of the code. |
anarnold97
reviewed
Jul 31, 2025
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| If you accept that the agentic AI must continue to make changes, it compiles the code and runs a partial analysis. In this phase, the agentic AI can detect diagnostic issues (if any) generated by tools that you installed in the VS Code IDE. You can accept the agentic AI's suggestion to fix these diagnostic issues too. After every phase of applying changes to the code, the agentic AI runs another round of automated analysis depending on your acceptance, until it has run through all the files in your project and fixed the issues in the code. Agentic AI generates a new file in each round when it applies the fix suggestions in the code. The time taken by the agentic AI to complete several rounds of analysis depends on the size of the application, the number of issues, and the complexity of the code. | ||
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| The RAG solution, delivered by the solution server, is based on solved examples or past analysis to fix new issues or similar issues that are found while analyzing the source code. This type of analysis is not iterative. The Solution server analysis generates a diff of the updated portions of the code and the original source code for a manual review. In such an analysis, the user has more control over the changes that must be applied to the code. |
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| The RAG solution, delivered by the solution server, is based on solved examples or past analysis to fix new issues or similar issues that are found while analyzing the source code. This type of analysis is not iterative. The Solution server analysis generates a diff of the updated portions of the code and the original source code for a manual review. In such an analysis, the user has more control over the changes that must be applied to the code. | |
| The RAG solution, delivered by the solution server, is based on solved examples or past analysis to resolve new issues or similar issues that are found while analyzing the source code. This type of analysis is not iterative. The Solution server analysis generates a diff of the updated portions of the code and the original source code for a manual review. In such an analysis, the user has more control over the changes that must be applied to the code. |
personal preference - resolve issues sounds better
anarnold97
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Jul 31, 2025
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