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Lyft’s iOS Testing Revolution: How They Made Testing Less Awful (And Why It Worked)
Let's be honest - iOS testing sucks. You write these giant test suites that take forever to run, then they break when someone changes a button color. Lyft's mobile team felt this pain BIG TIME - until they invented something called Focus Flows. Here's the messy story of how they fixed testing without making everyone hate their jobs.
Not gonna lie - even their diagrams look better than most apps
The Testing Nightmare
Back in 2021 Lyft's iOS situation was rough:
- Test suites took 45+ minutes to run (coffee break anyone?)
- Flaky tests failed 30% of builds for no reason
- New devs needed 2 days just to understand the test setup
One engineer joked: "Our tests tested our patience more than our code." Not ideal when you're trying to fix ride-sharing bugs at scale.
The Breaking Point
Three things forced change:
1. A critical payment bug slipped through tests 2. Team velocity dropped 40% due to test maintenance 3. Senior devs threatened to mutiny over CI failuresWhat Are Focus Flows?
Lyft's solution wasn't another fancy framework. Focus Flows are:
- Small: Test ONE user journey at a time
- Fast: Runs in <1 ci="" even="" li="" minute="" on=""> 1>
- Clear: Name explains what it tests (ex: "PaymentFailureFlow")
No more giant "testEverything" suites. Just laser-focused scenarios that actual match real user paths.
Building Focus Flows: The Good, Bad, and Ugly
Here's how they hacked it together:
1. The Setup Struggle
Old way:
// 🤮 Legacy test setup func testPayment() { let user = User() let payment = Payment() let ride = Ride() // 50 more lines... }
New Focus Flow way:
// 😍 Focused test func testFailedCardPayment() { startFlow(.paymentFailure) .enterTestCard(.declined) .verify(.errorMessageVisible) }
2. CI Pipeline Wars
They had to:
- Split tests into parallel buckets - Add auto-retry for legit flaky tests - Build a "test health" dashboardOne dev admitted: "We broke CI 3 times before getting it right. Worth it though."
The Results (That Actually Matter)
Metric | Before | After |
---|---|---|
Test Runtime | 45min | 8min |
Flakes | 30% | 4% |
Bugs in Prod | 15/month | 2/month |
Lessons Learned (The Hard Way)
- Tests ≠ Coverage Reports: 100 bad tests < 10 good ones
- Naming Matters: "testPaymentFlow" > "testCase47"
- Speed = Usage: Fast tests get run, slow ones get ignored
As the tech lead put it: "We stopped worshipping test coverage % and started caring about test impact."
What’s Next for Lyft Testing?
Current experiments:
- AI-generated test flows from user sessions - Predictive test selection (only run what changed) - Auto-healing tests that fix themselvesThey’re even open-sourcing some tools. Fingers crossed!
Why This Matters to You
"Good testing should feel like guardrails, not handcuffs." - Lyft Mobile Lead
Lyft proved that better testing isn’t about more tools - it’s about smarter workflows. And hey, if they can make iOS testing suck less, anyone can.
Read the full technical breakdown here: Lyft’s Focus Flows Deep Dive
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