Refactor E2E test suite to eliminate flakiness with deterministic retry and state isolation

[Smartdevs17]

Context

The SubTrackr Detox E2E test suite has inconsistent reliability—tests pass locally but fail in CI due to timing, network conditions, and shared state between test cases. Flaky tests reduce developer confidence, obscure real regressions, and increase CI cycle time.
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Current Limitation

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• Tests depend on implicit timing (hardcoded wait values)
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• Shared app state between test cases causes cascading failures
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• Network-dependent tests fail unpredictably under varying conditions
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• No hermetic test data seeding per test case
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• Screenshot baseline comparisons are brittle (pixel-perfect mismatches)
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Expected Outcome

A deterministic E2E test suite where each test case is fully isolated with hermetic data seeding, explicit wait strategies (expect-based, not timer-based), mock network layer for consistent responses, and visual regression tolerance thresholds.
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Acceptance Criteria

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• All test cases are fully isolated with independent app state initialization
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• Hardcoded wait() calls replaced with expectation-based waits
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• Mock network layer provides deterministic API responses for all test scenarios
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• Visual regression tests use configurable tolerance thresholds (not pixel-perfect)
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• Test suite runs with zero flaky failures across 5 consecutive CI runs
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• CI test execution time does not exceed current baseline
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• Documentation for writing deterministic E2E tests
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• Flaky test detection and automatic re-run with reporting
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Technical Scope

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• Files: e2e/ (all files), .detoxrc.js, .github/workflows/e2e-detox.yml, e2e/helpers/visualRegression.ts
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• APIs: Detox expect API, mock server for API responses, screenshot diff libraries (pixelmatch)
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• Edge cases: CI vs local environment differences (animations, keyboard, notifications), device simulator variations, JS timer precision, async data loading, gesture recording playback

[Abdulrasaq1515]

@Abdulrasaq1515 has applied to work on this issue as part of the Stellar Wave Program's 6th wave.

I want to refactor the E2E test suite because the current Detox tests are flaky — they pass locally but fail in CI due to timing, network conditions, and shared state. Refactoring with hermetic data seeding per test case, expectation-based waits (not hardcoded wait()), a mock network layer for deterministic responses, visual regression tolerance thresholds, and automatic retry with reporting will eliminate flakiness.

Relevant experience:

Detox E2E — fixed flaky E2E tests for 3 React Native apps; state isolation, mock network, visual regression tolerance

ℹ️ Repo Maintainers: To accept this application, review their application or assign @Abdulrasaq1515 to this issue.

[distributed-nerd]

@distributed-nerd has applied to work on this issue as part of the Stellar Wave Program's 6th wave.

can i work on this

ℹ️ Repo Maintainers: To accept this application, review their application or assign @distributed-nerd to this issue.

[drips-wave]

Congratulations, @distributed-nerd! 🎉 Your application was accepted by the repo's maintainers, and the issue is due on June 30, 2026.

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