The challenge in software version updates is rarely just moving a single library or technology to a newer version. Software is made up of many interdependent parts, so one change can affect other parts of the application as well. As update debt grows, that web of dependencies typically becomes more complex.
This is where our AI-based Renew modernization model offers a different approach.
AI helps identify software dependencies
In a traditional version update, a large share of the work goes into figuring out what needs to change, editing the code, and then finding and fixing the errors that follow. In an AI-driven model, these steps can be automated and handled systematically as one process.
AI analyzes the software’s dependencies, for example, and identifies the code changes a single update requires. Those changes can then be implemented in stages and validated as the work progresses.
This also matters for security. If the software’s libraries are badly outdated, fixing a known vulnerability may not succeed by updating one component alone. Updating one library can require several other updates and changes to the application code. When the chain of impact can be identified in advance, the update is more predictable and the risk of unexpected side effects is smaller.
Testing stays with the update throughout the process
Speeding up code changes is not enough to make a version update successful. What matters is being able to show that the system still behaves the same way after the changes.
That is why, in our Renew modernization model, we build comprehensive tests for the system before the actual updates. Tests can be run at different stages of the work, so problems surface immediately. Errors can be pinpointed as the work proceeds, instead of tracing them only after the update is complete.
The update also becomes a traceable whole. Changes can be documented so it is later clear what was changed, why, and which update it belonged to. That helps both with managing the current project and with later maintenance and further development of the system.
An up-to-date technology foundation reduces the risks of technical debt
AI’s biggest impact on version updates is not the automation of individual tasks. It is that the whole update process can be carried through more systematically, with less manual work.
That also changes how accumulated technical debt can be approached. Long-postponed updates no longer have to mean one heavy, hard-to-predict project. The work can be broken down in a more controlled way, stage by stage.
An up-to-date technology foundation is therefore not just a technical goal. It helps keep the system secure and maintainable, and it leaves more room to develop new features, integrations, and solutions that support the business.
Are software version updates a pressing issue for you? Get in touch, and we will look together at how version updates can be carried out with AI in a more controlled way, with less manual work.
