Shifting from Performance to Guardrails

Integrating recomposition tracking tools like Dejavu is often viewed through the lens of finding "performance disasters." However, in a mature codebase, the primary value is often operational: establishing guardrails. By defining explicit test contracts for recomposition, developers can ensure that future refactors or AI-generated code do not silently broaden recomposition scopes. The absence of a "big win" (i.e., finding a major performance bottleneck) does not mean the tool is ineffective; rather, it provides proof that existing state boundaries are sound and protects against future regressions.

Debugging Tooling Integration

When adopting inspection tools, developers often blame their own app architecture for failures. In this case, a recurring testTag mapping failure was initially misidentified as an issue with composable structure or screen complexity. By building a minimal reproduction—a single screen with two tagged composables and a state change—the author identified that the issue was actually an Android runtime edge case where Dejavu failed to map tags if it enabled after the activity had already resumed. This highlights the importance of isolating issues into a standalone repro rather than attempting to debug within a noisy, complex production app.

Pragmatic Tool Adoption

Successful tool adoption requires a shift in expectations. Instead of seeking immediate performance gains, treat the integration as a way to build "performance confidence." The author’s process involved:

• Targeting stateful screens where boundaries are critical.
• Performing app-side cleanup (extracting smaller, named composables) to improve testability.
• Contributing fixes back to the library (e.g., PR #52) to resolve runtime edge cases.
• Accepting that "no news is good news" when tests pass, as this confirms the current implementation is acceptable and creates a baseline for future maintenance.