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This is important because historically , it was a common practice for companies to customize their SAP applications by inserting their own code into them . This made it easy to add special features to the apps . But it also complicated application maintenance and platform upgrades because teams had to ensure that their custom business logic remained compatible with newer versions of SAP ’ s products – a process that required extensive regression testing and that could prevent successful upgrades in cases where custom code turned out to be incompatible .
By decoupling custom code from applications , the Clean Core initiative aims to make it easier for businesses to keep their core SAP environments consistent and standardized – and , by extension , to help to migrate them to the cloud , where compatibility with standardized environments is critical .
Custom application features will still be available , but through API-integrated microservices hosted on the SAP Business Technology Platform ( BTP ), which is separate from the SAP “ core .”
For businesses with complex SAP estates , the value of adopting a Clean Core approach is clear enough because it makes upgrades and cloud migration much simpler .
What ’ s much harder for most companies , however , is actually breaking custom code out of their SAP apps and turning it into microservices , due to the sheer volume of code at stake . Many of the businesses I work with have tens of thousands – and in some cases hundreds of thousands – of lines of custom code within their legacy apps . Refactoring all of that code manually would require thousands of days of work by skilled developers . That ’ s simply beyond the realm of feasibility for businesses whose development teams are already overstretched .
Using gen AI tools , we are prototyping a solution that analyzes legacy apps to identify custom code within them .
That ’ s why my team has turned to AI to help companies make the transition to Clean Core . Using gen AI tools , we are prototyping a solution that analyzes legacy apps to identify custom code within them . From there , it produces a specification document that describes what the custom code does . The document is then reviewed by process owners to confirm that the AI has accurately interpreted the purpose of the custom code .
So long as it has , the AI solution goes on to generate microservice code to run on SAP BTP , along with test scripts that can be executed against the new code to ensure smooth upgrades .
The result is the ability to shrink migration projects that would have required thousands of days of tedious legacy app refactoring work into initiatives that organizations can complete in a fraction of that time . And , because our AI solution produces test scripts as well as the microservice code , it ensures that teams can efficiently test their extensions against future platform upgrades , too .
But again , building a solution like this requires much more than simply asking generic AI tools to refactor code .
To ensure that the microservices code produced by our tools actually works as intended , we ’ re
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