Acceptance can set a modernization schedule when implementation arrives faster than the organization can establish that it meets the required contract. If code generation accelerates while requirements clarification, independent testing, and business decisions remain constrained, completed code can accumulate ahead of accepted functionality.
This is a conditional capacity argument. The research does not establish a universal AI productivity gain, a standard review ratio, or a measured bottleneck shift across modernization programmes.
Completion passes through several stages
A migration needs a defined behavior, an implementation, integration with its dependencies, evidence of correct operation, and decisions about discrepancies. Cutover and retirement add further obligations after individual code changes pass review.
Suppose a team can produce ten candidate changes in a period but can evaluate only four against independently confirmed business examples. In that constructed example, more candidate output alone cannot raise accepted output beyond the constrained evaluation stage.
The numbers are illustrative. Real stages overlap, changes vary in difficulty, and rejected work can return for revision. The mechanism remains that accelerating one stage helps the schedule only to the extent that other required work can absorb its output.
Plausible code still needs semantic evidence
GitHub’s Copilot Chat documentation warns that suggestions can be incomplete and require review and testing. Target-language syntax and successful compilation do not establish correct transaction boundaries, error behavior, or data interpretation.
Business-rule discovery can also remain incomplete. CodeQL’s analysis limits include unavailable library source and runtime-determined behavior, while business meaning needs interpretation beyond a code graph.
If a generated implementation reaches an unresolved rule quickly, generation has exposed a decision sooner. It has not supplied the missing policy or demonstrated what the application should do.
Generated tests can share the same bottleneck
Generating assertions alongside implementation can increase test volume without providing independent expected results. If both artifacts inherit the same wrong assumption, their agreement offers weak evidence about correctness.
PIT’s mutation-testing mechanism illustrates the distinction between executed code and detected faults. Tests need to reject meaningful wrong behavior, with interpretation of surviving or equivalent mutations within the tool’s scope.
Independent examples and accepted baselines can make this work reusable. Their creation, maintenance, and domain review are therefore possible capacity investments rather than administrative additions after coding.
Comparison requires controlled conditions
Source and target runs need aligned starting state, inputs, business dates, and configuration. Otherwise discrepancy review can spend time investigating differences created by the comparison itself.
Approved normalization should retain business-significant distinctions. Automatically accepting changed snapshots or loosening tolerances can increase the apparent acceptance rate while weakening the contract being verified.
A useful unit of progress is an accepted business behavior with declared coverage and resolved exceptions. Lines generated, files converted, and tests created describe other activities and should retain their narrower meanings.
Observe where work waits
A local assessment can track time spent implementing, waiting for fixtures, resolving rule questions, running comparisons, and obtaining decisions on discrepancies. It should distinguish active effort from queue time and control for changes in task mix.
The hypothesis weakens if implementation still dominates, if acceptance scales with generation, or if better tools reduce the evidence burden without weakening checks. It strengthens when reviewable candidates wait while the same scarce domain knowledge or test environment remains required.
The practical implication is to plan generation and acceptance together. Faster implementation can be valuable, but the schedule should follow demonstrated completion of the whole migration contract rather than the production rate of one artifact.