This contribution was submitted by Koen Vanderkimpen, IT consultant at Smals Research. Because of the relevant perspective and topicality of the topic, the editors are publishing it in full on ITdaily.
In the ever-changing world of software development, legacy code is a topic that is often discussed but rarely well understood. Legacy code can be defined in several ways, but for this discussion we consider it to be any code that is still being used by a major application, but should be upgraded. Perhaps a new architectural style or standards are needed, outdated frameworks are still being used, or the code is written in a programming language that is no longer supported. Or too many times the code has been out of the ground and has become cluttered 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 is not being addressed. Some also refer to this as technical debt.
But what if we go a step further and try to not only control this code, but proactively improve it? And can we take advantage of new technologies like AI to do this?
Why Legacy Code Is a Problem
Legacy code presents numerous challenges. The biggest of these is probably the fact that it slows down development and makes maintenance difficult; or it requires specialized knowledge of outdated tools. Without the right knowledge and tools, it can become a huge cost to any software company. Moreover, if the developers who wrote the original code have also left the company, the problem can become even bigger. Speaking of developers, technical debt is also one of the biggest annoyances for them in their jobs.
Fortunately, there are a number of ways to deal with these challenges, even using the most advanced technologies, such as artificial intelligence.
Strategies to Manage Legacy Code
There is, unfortunately, no magic solution that solves all legacy code problems at once. It requires a multi-faceted approach involving multiple tactics simultaneously. Consider analyzing the code, building new components in a modular fashion and continuously maintaining the codebase to ensure that it remains up-to-date and properly adapted to future needs.
A first step is to perform static analysis. This involves examining code without actually executing it in order to identify potential problems such as dependencies, security vulnerabilities, or so-called “code smells. Using static analysis tools, developers can better understand the structure of the code and identify problem areas that need improvement. Some of these tools are baked into a good IDE, but you can often add additional power to them.
After the code is analyzed, setting up testbenches is essential, following the Test Driven Development (TDD) paradigm. 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 made. Moreover, if there is sufficient test coverage, it becomes a lot safer to refactor the code, which we will need to continue to do throughout the lifecycle to ensure that the code keeps getting better and better and certainly does not reach legacy status. Setting up testbenches also additionally supports continuous integration and deployment (CI/CD) processes, which are essential for long-term quality management.
Another important strategy is to refactor the code to make it modular. By breaking down a monolithic codebase into smaller, more manageable modules, developers can isolate functionalities, making the code easier to understand, maintain and test (and possibly even rewrite). This modularization also allows different teams to work simultaneously on different parts of the codebase, which increases productivity and reduces the likelihood of errors.
The Role of AI in Addressing Legacy Code
Artificial intelligence offers new opportunities for managing legacy code, especially now, with the rapid evolution of generative AI. Although AI’s current capabilities are often limited to small-scale tasks, its potential to transform legacy code management is significant. AI can help analyze code, write tests, refactor and even generate documentation. This makes AI a valuable tool for developers.
- Analyze Code: AI tools can analyze code to determine structure and functionality, providing insights that may not be immediately apparent to human developers. This can be especially 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 overlooked during manual testing.
- Code Refactors: AI-driven tools can suggest improvements and help developers refactor code incrementally. This continuous refactoring process helps keep the codebase clean and maintainable, reducing technical debt over time.
- Documentation Generation: AI can automatically generate and maintain up-to-date documentation as part of CI/CD, which is especially useful in large projects where maintaining documentation can be challenging.
These tasks can mostly be performed by the same type of AI tools: so-called code assistants. 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 emergence 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 legacy code management is only likely to increase, providing even more opportunities for improving software development processes.
However, we should not blind ourselves to AI either: the best medicine against legacy code is to avoid it, and this by applying the right tactics throughout the software development lifecycle to keep the design and code as clean and up-to-date as possible. In a future blog post, we will elaborate on this, zooming in on the different phases of the lifecycle to discuss how to avoid legacy, and also how AI can be used during each phase as a tool to do so.
This is article is a contribution by Koen Vanderkimpen, IT consultant at Smals Research. This article was written in his own name and does not take a position on behalf of Smals. Interested in working at Smals? Then take a look at the current extensive job offerings.