This post was submitted by Koen Vanderkimpen, IT consultant at Smals Research. Due to the relevant perspective and topicality of the topic, the editorial team publishes it in its entirety on ITdaily.

In the ever-changing world of software development, legacy code is a topic that is often discussed but rarely truly understood. Legacy code can be defined in a variety of ways, but for this discussion we will consider them as such Any code that is still used by a main application but would need to be updated. Maybe a new architectural style or standards are required, outdated frameworks are still used, or the code is written in a programming language that is no longer supported. Or, all too often, the code has not been thoroughly edited and is contaminated with quick fixes. In other words, it is code that is becoming increasingly difficult to maintain and needs to be rewritten, but for some reason it is not being considered. Some also mention this technical debt.

But what if we go one step further and try to not only control this code, but also proactively improve it? And can we use new technologies like AI?

Why legacy code is a problem

Legacy code presents numerous challenges. The biggest of these is probably the fact that it slows development and makes maintenance difficult; or it requires special knowledge of outdated tools. Without the right knowledge and tools, this can become a huge cost for any software company. If the developers who wrote the original code have also left the company, the problem can become even worse. Speaking of developers: Technical debt is also one of the biggest annoyances in their work.

Fortunately, there are a number of ways to overcome these challenges, even with cutting-edge technologies like artificial intelligence.

Strategies for managing legacy code

Unfortunately, there is no magic solution that will solve all legacy code problems at once. It requires a multi-pronged approach using multiple tactics simultaneously. This includes analyzing the code, building new components modularly, and continually maintaining the code base to ensure it remains current and well-adapted to future needs.

After the code is parsed, it is Setting up test benches According to the Test Driven Development (TDD) paradigm, this is essential. This creates a safe environment in which various tests can be run to ensure that the code behaves as expected. Testing is crucial to detect bugs and performance issues early before changes are implemented. When there is sufficient test coverage, it becomes much safer to refactor the code, which we must continue to do throughout the lifecycle to ensure that the code continues to improve and certainly does not become obsolete. Setting up test benches also supports continuous integration and delivery (CI/CD) processes, which are essential for long-term quality management.

Another important strategy is to refactor the code accordingly modular make. By breaking a monolithic codebase into smaller, more manageable modules, developers can isolate functionality, making the code easier to understand, maintain, and test (and perhaps even rewrite). This modularization also allows different teams to work on different parts of the codebase at the same time, increasing productivity and reducing the risk of errors.

The role of AI in tackling legacy code

Artificial intelligence offers new possibilities for managing legacy code, especially now with the rapid development of generative AI. While AI’s current capabilities are often limited to small tasks, its potential to transform the management of legacy code is significant. AI can help analyze code, write tests, refactor, and even create documentation. This makes AI a valuable tool for developers.

• Code analysis: AI tools can analyze code to determine its structure and functionality, providing insights that may not be immediately apparent to human developers. This can be particularly useful when dealing with complex or poorly documented legacy systems.
• Writing tests: AI can also help automatically generate tests based on code behavior, saving time and improving code quality by identifying edge cases that may be missed during manual testing.
• Code refactoring: AI-powered tools can suggest improvements and help developers incrementally refactor code. This continuous refactoring process helps keep the codebase clean and maintainable and reduces technical debt over time.
• Create documentation: AI can automatically generate and maintain up-to-date documentation as part of CI/CD, which is particularly useful for large projects where maintaining documentation can be challenging.

These tasks can often be performed by the same type of AI tools: the so-called Code Wizards. Some examples are Codacy, Snyk and of course GitHub Copilot.

Conclusion: The future of legacy code management with AI

Managing legacy code remains a complex challenge that requires a strategic approach. But with the rise of AI as a tool for analyzing, testing, and refactoring code, opportunities are emerging to address this challenge more efficiently and cost-effectively. Generative AI technology is still evolving and its role in managing legacy code is only likely to increase, providing even more opportunities to improve software development processes.

However, we shouldn’t blindly focus on AI: the best medicine against stale code is to prevent it by using the right tactics throughout the software development lifecycle to keep design and code as clean and up-to-date as possible. We’ll delve deeper into this in a future blog post, taking a closer look at the different stages of the lifecycle to discuss how we can avoid legacy issues and also how AI can help at each stage.

This article is contributed by Koen Vanderkimpen, IT consultant at Smals Research. This article was written on my own behalf and does not represent any position on behalf of Smals. Are you interested in working at Smals? Then take a look at the current extensive job offer.