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Reviving Legacy Systems: The Role of GitHub Copilot and AI

Discover how GitHub Copilot and AI agents are modernizing legacy COBOL systems for today鈥檚 developers. Read more on The GitHub Blog!

Reviving Legacy Systems: The Role of GitHub Copilot and AI

How GitHub Copilot and AI Agents are Saving Legacy Systems

In the rapidly evolving landscape of technology, legacy systems often pose a significant challenge for organizations. As we look towards 2025, the need to modernize these systems has become increasingly urgent, particularly for those built on aging programming languages like COBOL. Despite being over 65 years old, COBOL is still the backbone of many critical systems, including banking and government operations. The shortage of COBOL developers has created a significant barrier, but with innovations like GitHub Copilot and AI agents, there鈥檚 a new hope for legacy modernization.

The Challenge of Legacy Systems

Many organizations are running on COBOL, with approximately 200 billion lines of code still in active use. However, as original developers retire, finding someone who can maintain or modernize these systems is becoming increasingly difficult. This has led to a situation where companies must rely on outdated technology without the necessary expertise to update it. The urgency to modernize is clear, as these legacy systems are crucial for daily operations.

The Need for Modern Solutions

For developers entering the field today, the idea of working with COBOL can seem daunting, if not impossible. However, the introduction of AI tools like GitHub Copilot has transformed this landscape. These tools allow modern developers to interact with legacy code without needing extensive knowledge of the original programming languages. This shift not only democratizes access to legacy systems but also enables a new generation of developers to contribute to their modernization.

Meet the Developer Transforming COBOL

Julia Kordick, a Microsoft Global Black Belt, is one of the pioneers in utilizing AI to modernize COBOL systems. Remarkably, Julia has never learned COBOL. Instead, she leveraged her expertise in AI and collaborated with seasoned COBOL developers. This partnership exemplifies a modern approach to tackling legacy systems: combining domain knowledge with cutting-edge technology.

Bridging the Knowledge Gap

Julia's approach highlights a critical insight: you don鈥檛 need to be a COBOL expert to modernize COBOL systems. By focusing on designing intelligent solutions and working with those who possess deep domain expertise, Julia and her team have developed a powerful framework for legacy modernization.

The Three-Step Framework for AI-Powered Legacy Modernization

Julia and her team have crafted a systematic approach to legacy modernization that is not only applicable to COBOL but can be adapted to any legacy system. This three-step framework leverages the capabilities of GitHub Copilot and AI agents.

Step 1: Code Preparation (Reverse Engineering)

  • The first step in the framework involves reverse engineering the existing code. This is crucial because many organizations lack comprehensive documentation of their legacy systems. By utilizing AI tools like GitHub Copilot, developers can analyze the codebase, understand its functionality, and identify areas that require modernization.

  • For example, an AI agent can automatically generate documentation based on the existing code, allowing teams to visualize the structure and flow of the system without needing to sift through thousands of lines of code manually.

Step 2: Code Translation

  • Once the code is prepared, the next step is translating the legacy code into a more modern programming language or framework. GitHub Copilot can assist in this process by suggesting code snippets and best practices, making it easier for developers who may not be familiar with COBOL to create equivalent functionality in a modern language.

  • For instance, if a banking application processes transactions using COBOL, an AI agent can help convert this functionality into a microservices architecture using a language like Python or Java, streamlining the process and enhancing maintainability.

Step 3: Integration and Testing

  • The final step involves integrating the new code with existing systems and thoroughly testing it to ensure functionality. AI agents can automate testing processes, identifying bugs and performance issues quickly.

  • By utilizing AI for testing, teams can significantly reduce the time it takes to validate that the new system meets business requirements, allowing for a smoother transition from legacy to modern systems.

Practical Implications

The implications of using GitHub Copilot and AI agents for legacy system modernization are profound. Organizations can reduce their reliance on niche COBOL developers, streamline the modernization process, and ultimately save costs. This approach also allows for a more inclusive workforce, as it opens the door for a broader range of developers to contribute to projects that were previously out of reach.

Moreover, the combination of AI and human expertise fosters a collaborative environment where knowledge transfer can occur. Legacy system experts can guide modern developers, ensuring that critical business knowledge is not lost while simultaneously empowering a new generation of tech talent.

Conclusion

As we continue to navigate the complexities of legacy systems, the integration of AI technologies like GitHub Copilot represents a significant shift in how organizations can approach modernization. By leveraging AI, companies can bridge the gap between outdated systems and modern development practices, ensuring that critical infrastructure remains functional and accessible. The future of legacy system modernization is here, and it鈥檚 powered by collaboration, innovation, and a bit of AI magic.

Frequently Asked Questions

GitHub Copilot can help developers by suggesting code snippets and automating repetitive tasks, making it easier to update and maintain legacy COBOL systems. This enables teams to work more efficiently and reduces the time needed to implement modern features.
AI agents can analyze existing codebases and provide insights on potential improvements or refactoring opportunities. When combined with GitHub Copilot, they can enhance productivity by generating context-aware suggestions that align with the specific needs of legacy systems.
Yes, one challenge is that legacy code often lacks documentation, making it harder for AI tools to understand the context. Additionally, developers may need to invest time in training AI models to ensure they align with the specific coding standards and practices of the legacy system.