[Progress Blogs] How AI Can Help Developers Understand and Debug Existing OpenEdge Applications

Writing new code is only one part of maintaining business-critical applications. For many Progress OpenEdge developers, the harder challenge is understanding an existing ABL codebase, tracing unexpected behavior and making changes without disrupting the business processes that depend on it.

Applications built over many years can contain extensive business rules, shared procedures and dependencies that are not immediately obvious to someone investigating a problem. AI-assisted development offers another way to explore that complexity. Rather than starting with a blank file, developers can use AI to examine what is already there, ask targeted questions and narrow the search for the cause of an issue.

Why Understanding Existing ABL Code Can Be Difficult


Long-lived OpenEdge applications often reflect years of enhancements, integrations and decisions made by different development teams. A single workflow may span procedures, classes, database queries and application-specific conventions. Documentation may not fully explain why a particular rule exists or which other processes rely on it.

That makes even a seemingly small change an investigation. Developers need to understand not only what a section of code does, but also when it runs, what data it changes and how it interacts with the rest of the application. AI can assist with this initial discovery by explaining code paths, identifying related routines and highlighting assumptions worth checking.

Use AI to Investigate Before Changing Code


One useful starting point is to ask an AI coding assistant to summarize a procedure or class and explain its inputs, outputs and dependencies. From there, developers can ask more specific questions: Where is a field updated? Which routines call this procedure? What conditions determine whether a transaction is committed? These questions turn code comprehension into a focused investigation.

The quality of the results depends on the context available to the assistant. A snippet may explain local behavior, while understanding a complete workflow often requires related files, schema information and project conventions. Developers should treat AI explanations as hypotheses to verify against the actual code, not as a substitute for reviewing how the application executes.

Track Down Difficult Bugs with Better Context


AI can also help when a problem is difficult to reproduce or its cause is not obvious. Instead of asking an assistant to fix a vague error, developers can describe the behavior they observe, the result they expect, the steps that trigger the issue and any relevant logs or code. This gives the assistant a clearer basis for suggesting where to investigate.

For example, if an application returns an unexpected value after a particular update, AI might help trace assignments, review conditional branches or identify code paths that handle the same field differently. The developer can then test those possibilities using existing debugging tools and a reproducible case. This approach is especially useful for narrowing the scope of an investigation before making changes.

Uncover Inherited Business Logic


Not every problem is a technical defect. Sometimes the challenge is understanding a business rule that was implemented years ago and is now buried within application logic. A condition may look unnecessary until a developer discovers that it supports an exception for a particular customer, transaction type or operational process.

AI can help developers trace those rules across related code and formulate questions for the people who know the business. It can surface branches, dependencies and unusual conditions that deserve closer inspection. The important distinction is that AI can help find and explain an apparent rule, but only the organization can confirm whether that rule remains correct and necessary.

Audit Coding and Modernization


Understanding existing behavior is also a practical first step toward modernization. Before refactoring a procedure, replacing an interface or reorganizing a service, teams need to know which behaviors must remain unchanged. AI-assisted code review can help identify duplicated logic, tightly coupled routines and areas where additional tests would reduce risk.

Developers can use the findings to prioritize a manageable sequence of changes. They might first document a complex workflow, add tests around its current behavior and then refactor one component at a time. This makes AI a tool for building confidence in modernization decisions rather than a shortcut that bypasses analysis.

Keep Developers in Control of the Results


AI-generated explanations and fixes still require validation. An assistant may miss a dependency, misunderstand an ABL construct or suggest a change that appears correct in isolation but conflicts with an established business rule. Teams should review suggested edits, test edge cases and check performance and security implications before applying changes to production systems.

A practical workflow is to begin with a specific question, provide only the relevant and approved project context, ask the assistant to explain its reasoning and identify the code involved, then verify the findings with debugging and automated tests. Sensitive source code and data should be handled according to organizational policies and the configuration of the chosen AI service.

Explore AI-Assisted Development with the OpenEdge AI Assistant


AI-assisted development can deliver value long before developers begin writing new features. For organizations maintaining established OpenEdge applications, AI can help developers understand unfamiliar code, investigate difficult problems and uncover business logic that might otherwise take considerable time to trace. Combined with developer expertise, debugging tools and testing, these capabilities can help teams maintain and modernize existing applications with greater confidence.

The goal is not to replace developers' knowledge and experience, but to help them spend less time navigating complex code and more time improving the applications their organizations depend on. To learn how AI can support your ABL development workflows, explore the Progress OpenEdge AI Assistant and discover how AI-assisted tools can help developers work more effectively with existing OpenEdge applications.

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