SAP Custom Code Remediation Estimator
Custom code is usually one of the largest uncertain lines in an S/4HANA business case. This estimator converts object counts, usage and ATC findings into hours, cost and elapsed time, so you can size the work before the technical analysis finishes.
Your numbers
Programs, classes, function modules, reports, enhancements and forms.
From usage data such as ABAP call monitor over several months.
From S/4HANA readiness checks in ABAP Test Cockpit.
Quick fixes and automated corrections.
Unit and integration testing relative to development hours.
Your results
Rough planning model only. Real effort depends on code complexity, modifications of standard objects, data model changes and tooling. Re-estimate after running ABAP Test Cockpit checks on your system.
Total hours as share with findings rises
Sweeps Share of used objects with findings from 5 to 80, holding your other inputs fixed.
Custom code triage on private infrastructure
Talk about running code analysis and fix suggestions on a private model so your ABAP stays in your environment. Optional.
Optional. The tool and every export work without this. No spam.
How to use the estimator
Start with the total count of custom objects in your ECC system. Enter the share actually used, which you can measure with usage collection such as the ABAP call monitor over a full business cycle. Then enter the share of used objects that carry findings from S/4HANA readiness checks in ABAP Test Cockpit. Add the fraction fixable with tooling, your average manual fix time, the testing ratio, the blended rate, and your team size. The results show hours, cost and duration, with a chart showing how sensitive the hours are to the findings share.
- Measure usage over at least one full year-end cycle
- Use actual ATC findings once available
- Update the average hours per fix after a pilot batch
- Adjust team size to see duration effects
The method
The logic is a funnel. Total objects are reduced to those in use, then to those with findings, then split into automated and manual fixes. Development hours are manual fixes times average time, plus a short review per automated fix. Testing is added as a ratio of development hours. Cost is hours times rate, and duration divides hours by team capacity. Retiring unused code is the cheapest remediation, which is why usage data matters more than almost any other input.
Reading the result
Compare the total hours with your team capacity to see whether remediation fits the timeline. The sensitivity chart shows how steeply effort rises with findings; if you are uncertain about the share with findings, estimate at three levels. Treat the output as a range, not a promise. A finding in a simple report is quick, while one in a heavily used pricing or posting enhancement can take days and require business retesting.
Common mistakes
The most expensive mistake is remediating code nobody uses. The next is ignoring modifications to SAP standard objects, which are harder than Z code and should be minimised. Teams also forget testing, regression and the fact that fixes for data model changes, such as the material number length or Business Partner, ripple across interfaces. Keep a buffer for items discovered late.
Frequently Asked Questions
How do I find out which custom code is used?
Use usage collection tools such as the ABAP call monitor, or SAP usage and procedure logging, over a full business cycle including period and year-end. Objects with no calls in that window are candidates for retirement, after a business check.
What does ABAP Test Cockpit check for S/4HANA?
ATC can run S/4HANA readiness check variants that flag code affected by simplification items, such as changed or removed tables and function modules. Use the check variant that matches your target release and review the findings by category.
Can AI help with remediation?
It can speed up analysis and draft fixes for common patterns, but each change still needs review and testing by an ABAP developer. AI is more reliable on repetitive findings than on complex business logic. Treat savings as a range, not a given.
Why 120 productive hours per month?
It reflects typical effective development time after meetings, reviews, environment issues and leave. Adjust the team size input if your own measured productivity differs. The duration result scales directly with the team.
If you want an automated scan, usage analysis or an AI-assisted fix workflow on your own code, we can discuss options on private infrastructure. The estimator works without us.
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