The need to modernize legacy systems has not been greater. As a generation of Cobol programmers retires, GenAI is emerging as a great support for modernizing systems, interpreting code, and filling the knowledge gap for younger developers.
The widespread presence of legacy code, coupled with the retirement of the developers who coded it, highlights an imminent need to eliminate legacy code. Migrations have always been a delicate process, stimulating a large market for migration tools. Now GenAI can be an even more dynamic tool to break us out of the legacy software cycle.
GenAI in the understanding phase of a legacy migration
A technology migration must start with understanding the current configuration. DevOps best practices recommend writing thorough documentation, but there are many cases where developers leave nothing but puzzling code behind.
A developer in the understanding stage of a legacy migration needs to find out what a previous engineer did. GenAI can help developers understand undocumented code by acting as a translator to interpret legacy code. It can help unravel system logic and dependencies, even when there is no sharing of knowledge from previous developers. In short, where there is incomplete documentation, AI can translate and interpret older code .
How to use GenAI to build dynamic migration and refactoring plans
GenAI can create dynamic refactoring and migration plans by conducting simulations to analyze dependencies and exploring various what-if scenarios. This approach allows prosecutors to make informed decisions, just as policymakers would when dealing with complex issues. Of course, migrations occur in non-production environments to avoid instances of catastrophic downtime.
Navigating a legacy migration is like tackling a complex Sudoku puzzle, where attempting a solution can lead to errors that force you to backtrack. Similarly, in migration, although many paths may seem viable, some lead to dead ends, requiring developers to retrace their steps. With GenAI, developers can project the consequences of code changes and migration steps, making the process more efficient and reducing the risk of unexpected problems in the future.
Watch an IBM expert delve into the different ways GenAI has contributed to the modernization of legacy systems. Although it is covered as “application modernization,” this same information applies to all legacy software, from reverse engineering to code debugging.
Reduce testing time with GenAI
GenAI can be an advantage for navigating the testing phase of legacy migration. Its ability to juggle multiple scenarios reduces the time developers spend on scenario testing. GenAI integration enables simultaneous testing, providing real-time support and suggestions to optimize migration processes. The complexities involved in testing during migration can be overwhelming, with countless variables to consider and potential pitfalls to avoid.
By enhancing developers' capabilities with real-time insights and proactive issue identification, GenAI improves the accuracy and efficiency of the migration process. This ensures a smoother transition to modernized systems.
Mitigate risks and maximize opportunities
Legacy systems need urgent modernization to mitigate vulnerabilities, introduce new features, and ensure compatibility with cloud-enabled tools. GenAI emerges as a transformative solution, offering efficiency and reliability in legacy migrations. In your project, try using it to interpret undocumented code workaround dependencies. It is also useful for performing multiple migration simulations for smoother upgrades.
Be aware of potential risks such as intellectual property infringement. You must also perform thorough due diligence to verify the accuracy of the data generated by GenAI. Having your developers as the final decision makers will avoid the loss of human oversight.
Using third-party AI solutions that consume sensitive business data may compromise information. For example, Azure OpenAI is powered by the same technology as ChatGPT, but gives companies more control over how their data is used. Mitigating risks with GenAI best practices will also contribute to smoother legacy upgrades.
If we are successful in employing GenAI as a more powerful migration tool to those currently on the market, we could completely free ourselves from the next generation of legacy systems.
