The Progress® OpenEdge® AI Coding Assistant is a specialized tool designed to support Advanced Business Language (ABL) developers working within the OpenEdge ecosystem. It provides developers with intelligent, context-aware assistance for writing, reviewing, and modernizing ABL code.

OpenEdge ABL developers often encounter challenges when trying to use AI for code quality improvement. Existing large language models (LLMs) have little to no knowledge of ABL, and training or fine-tuning custom LLMs requires large datasets and specialized expertise that are not readily available. By leveraging purpose-built generative AI (GenAI) capabilities, the OpenEdge AI Coding Assistant streamlines development workflows and automates repetitive tasks such as code completion, review automation, documentation building, unit test creation, code quality checks, security scans, CI/CD improvements, and legacy code modernization.

Here are some of the key features of the OpenEdge AI Coding Assistant:

  • IDE integration and tool-agnostic setup—Supports integration with multiple Integrated Development Environments (IDEs) to help developers stay in flow and benefit from AI-driven workflows and rule-based enhancements without needing to switch tools.
    Note: This guide uses Windsurf as the customized IDE for demonstrating AI-powered code assistance with ABL. However, the underlying setup is designed to be tool-agnostic, supporting integration with a variety of development environments, including Visual Studio Code, Windsurf, Cursor, GitHub Copilot, and more.
  • OpenEdge-specific AI code suggestions—Provides AI-powered code suggestions tailored to OpenEdge syntax and business logic.
  • Layered rule enforcement—Enforces layered rules by incorporating customer-specific, framework, and ABL requirements ensuring that code suggestions are both relevant and high-quality.
  • Flexible LLM selection—Provides multi-LLM support to help developers choose between models like ChatGPT, Claude Sonnet, and more.
  • Linting and modernization—Provides real-time linting by analyzing source code to detect errors, bugs, stylistic issues, or deviations from coding standards. It also offers modernization support for upgrading to OpenEdge 12 thereby improving ABL code maintainability, performance, and security.
  • Context-aware assistance—Provides context-aware assistance using Retrieval-Augmented Generation (RAG), which is an AI architecture that enhances the output of LLMs by retrieving relevant information from external sources, such as internal documentation, databases, or knowledge graphs before generating a response. OpenEdge AI Coding Assistant grounds AI responses in your actual codebase, documentation, and rules for more accurate, context-aware, and verifiable response.
  • Use of Model Context Protocol (MCP)—Ensures standardized integration of the OpenEdge AI Coding Assistant across IDEs such as Visual Studio Code, Windsurf, and Cursor. It also facilitates future extensibility by providing a flexible framework for incorporating new tools and evolving development environments without requiring major architectural changes.

Unlike generic coding assistants, this toolkit is tuned specifically for unique patterns and ABL-specific practices of OpenEdge.