E2E Test Fix: Mock Data Mismatch After Lists Implementation

by Sebastian Müller 60 views

Hey guys! Let's dive into fixing these pesky E2E test failures that popped up after implementing the Lists resource type. It’s like, we fixed one thing, and a bunch of new things showed up – classic, right? This article will break down the issues, proposed solutions, and how we're going to make sure our CI/CD pipeline is rock solid.

Executive Summary

So, we successfully implemented the Lists resource type (Issue #470), and we initially knocked out 18 failing tests by making our error messages better and beefing up our mock data setup. But guess what? New E2E test failures have decided to crash the party. We need to sort these out ASAP to keep our CI/CD pipeline running smoothly.

Current Test Status:

  • ✅ Unit Tests: 978/978 passing (100% success rate) – Woohoo!
  • ❌ E2E Tests: ~17+ failures across multiple test suites – Not so woohoo.
  • Impact: Tests pass at first, but then fail on assertions because our mock data structure is all out of whack.

Problem Analysis

Root Cause Discovery

After rolling out Lists and patching up those initial test hiccups, we’re now wrestling with a mock data structure misalignment. The operations themselves are going smoothly (mock data is being created just fine), but the test assertions are throwing tantrums because the returned structures don't match what the tests are expecting. It's like ordering a pizza and getting a calzone – technically food, but not what you wanted.

Technical Investigation

Debug logs show successful mock creation:

stderr | test/e2e/suites/tasks-management.e2e.test.ts
{
  "message": "Operation successful: execute",
  "data": {
    "success": true,
    "hasContent": true,
    "contentLength": 1,
    "resultType": "array"
  }
}

But the assertions? Not so happy:

× Task 0 should have content/title: expected undefined to be defined
× Task 0 should have content or title: expected undefined to be defined

Detailed Failure Breakdown

1. Tasks Management E2E Tests (17 failures)

File: /test/e2e/suites/tasks-management.e2e.test.ts

Primary Issue: Mock Task Structure Mismatch

The main keyword here is Mock Task Structure Mismatch. This is where our troubles begin. Currently, our mock task implementation, specifically in /src/handlers/tool-configs/universal/shared-handlers.ts:1256-1271, is causing some serious headaches. The crux of the issue lies in the fact that while the mock data is being created, the structure it adheres to doesn't quite align with what our tests are expecting. We're seeing operations succeed, which is great, but the subsequent test assertions? They're failing miserably because the returned structures simply aren't playing ball with our expectations. It's akin to building a house with the right materials but the wrong blueprint. The house might stand, but it won't pass inspection.

Here’s the current mock implementation that's causing the fuss:

const mockTask = {
  id: { task_id: '12345678-1234-1234-1234-789abcdef012' },
  content: content,  // ❌ Only content field provided
  is_completed: false,
  deadline_date: null,
  linked_records: [],
  assignees: [],
  created_at: new Date().toISOString(),
  // Missing: title field expected by tests
};

Now, let's compare this to what our tests are actually expecting:

// Tests expect BOTH content AND title fields
expect(task.content || task.title).toBeDefined();

See the problem? Our tests are expecting both content and title fields, but our mock only provides the content field. This discrepancy is the core of our mock task structure mismatch. It’s like showing up to a party with only one shoe – you're technically present, but something’s definitely missing. This seemingly small oversight cascades into a multitude of test failures, undermining the reliability of our entire testing suite. Addressing this misalignment is not just about fixing tests; it's about ensuring the integrity and dependability of our development pipeline.

Specific Test Failures:

  • ❌ Task content/title validation (undefined fields) – This is the big one, stemming directly from the missing title field.
  • ❌ Task creation with various parameters – When you can't even get the basics right, everything else crumbles.
  • ❌ High priority task creation – Because priorities don’t matter if the task structure is off, am I right?
  • ❌ Complete task workflow execution – The whole workflow falls apart when the initial data structure is incorrect.
  • ❌ Task priority lifecycle changes – Can’t change what isn’t there!
  • ❌ Concurrent task operations – Doing it wrong in parallel doesn't make it right.
  • ❌ Invalid task ID error handling – Even error handling suffers from the structural issues.
  • ❌ Task cleanup tracking validation – We're losing track of what to clean up because of this mess.
  • ❌ Task structure consistency – Consistency? What's that?

2. Lists Management E2E Tests (~3 failures)

File: /test/e2e/suites/lists-management.e2e.test.ts

Issues:

  • ❌ Retrieve all available lists – assertion failure
  • ❌ Invalid list ID error handling – message pattern mismatch
  • ❌ Invalid list ID operations – error format issue

Root Cause: Here, the root cause is that our error messages aren't matching the expected regex patterns (/not found|invalid|does not exist/i). It's like the tests are expecting a specific flavor of error, and we're serving up something completely different. This mismatch throws off the assertions, causing the tests to fail even though the underlying operation might be functioning correctly. It’s a bit like having a translator who speaks the wrong dialect – the message is there, but it's not being understood.

To dig a bit deeper, imagine you're ordering a coffee and you expect the barista to say, "Sorry, we don't have that." But instead, they say, "That coffee does not exist here." Both convey the same message, but if your ear (or test) is tuned to expect the former, you'll miss the meaning. In our case, the tests have specific regex patterns to match, ensuring error messages are not only present but also formatted correctly. When our error messages deviate from these patterns, the tests flag them as failures. This isn't necessarily an indication of a bug in the core logic, but rather a discrepancy in how errors are reported.

Fixing this means standardizing our error message formats so they align with the test expectations. This involves reviewing the error messages being generated in the code and ensuring they match the patterns the tests are looking for. It’s a bit like fine-tuning a musical instrument – small adjustments can make a big difference in harmony. By addressing this root cause, we ensure our tests accurately reflect the behavior of our application and provide a reliable safety net for future changes.

3. Notes Management E2E Tests (~2 failures)

File: /test/e2e/suites/notes-management.e2e.test.ts

Issues:

  • ❌ Test company creation – undefined ID object
  • ❌ Test people creation – undefined ID object
  • ❌ Non-existent record retrieval – error pattern mismatch
  • ❌ Cleanup tracking validation – count mismatch

Root Cause: The root cause for these failures primarily boils down to issues with our test data setup and cleanup mechanisms. Specifically, we're seeing problems where the test data setup is returning undefined IDs, and our cleanup tracking is malfunctioning. It's as if we're trying to build a house on a foundation that isn't properly laid – the whole structure is bound to wobble. The undefined IDs during company and people creation mean that subsequent operations relying on these IDs are likely to fail, leading to a cascade of errors. Similarly, the cleanup tracking malfunction suggests that we're not properly managing the lifecycle of our test data, potentially leaving behind remnants that could interfere with future tests.

To illustrate, imagine you're setting up a domino run. If some of the dominoes are missing (undefined IDs), the chain reaction will inevitably break. And if you don't properly reset the setup after the run (cleanup tracking malfunction), the next run might start with a jumbled mess. In the context of our tests, this means that our tests might be failing not because of the code itself, but due to the inconsistent or incomplete test environment.

Addressing this root cause requires a meticulous review of our test data setup and cleanup processes. We need to ensure that all necessary IDs are properly generated and available, and that our cleanup mechanisms are reliably removing test data after each run. It’s a bit like being a meticulous chef – you need to ensure all your ingredients are fresh and your kitchen is spotless before you start cooking. By fixing these issues, we create a more stable and predictable testing environment, allowing us to focus on the actual functionality of our code.

4. Universal Tools E2E Tests (~1 failure)

File: /test/e2e/suites/universal-tools.e2e.test.ts

Issue:

  • ❌ Non-existent record handling – unexpected error flag

Root Cause: The root cause here circles back to our mock data not being properly utilized, which is causing real API calls with fake UUIDs. It's like our tests are occasionally peeking behind the curtain and interacting with the live environment when they shouldn't be. This is problematic because E2E tests should operate in a controlled environment, isolated from external factors. When our mock data isn't being correctly used, our tests end up making API calls with bogus UUIDs, leading to unexpected errors and test failures. It’s a bit like going to a costume party but accidentally showing up in your everyday clothes – you're not quite fitting into the scene.

To put it another way, imagine you're rehearsing a play, and suddenly a real audience member wanders onto the stage. The actors would be thrown off, and the rehearsal would no longer accurately reflect the final performance. Similarly, when real API calls sneak into our E2E tests, they introduce variables that we're not prepared to handle, leading to unpredictable results. This not only causes test failures but also undermines the reliability of our entire testing process.

The solution lies in reinforcing our mock data usage and ensuring that our tests remain within the simulated environment. This may involve revisiting our environment detection mechanisms and making sure they're robust enough to prevent unintended API calls. It’s a bit like building a fortress around our test environment – keeping the real world out so we can focus on our simulations. By addressing this root cause, we can ensure that our tests are truly testing our code in isolation and providing us with accurate feedback.

Technical Debt Context

This whole situation is a classic case of technical debt. We rushed to implement the mock data infrastructure to fix those original 18 test failures, and while it worked in the short term, it’s now biting us in the butt. Here’s the breakdown:

  1. Mock Data Schema Misalignment: Quick mock implementations don't match exact test expectations.
  2. Test Environment Coupling: Production code contains test-specific logic that's fragile.
  3. Inconsistent Error Handling: Different error message formats across test scenarios.
  4. Missing Field Mapping: Test assertions expect fields that conversion functions don't provide.

Root Cause Analysis

Mock Data Evolution Gap

The convertTaskToRecord() function and mock data structures were slapped together to fix immediate test failures, but they didn't account for all test assertion requirements. It's like patching a hole in a dam with bubblegum – it might hold for a bit, but it’s not a long-term solution.

  1. Field Name Confusion: Tests expect both content and title, but the mock only provides content.
  2. Conversion Loss: convertTaskToRecord() may not preserve all expected fields.
  3. Test Detection Issues: Some tests may not be properly detected as running in a test environment.
  4. Error Format Drift: Error message formats have diverged from test expectations.

Proposed Solution

Okay, so how do we fix this mess? We’re breaking it down into phases to make it manageable.

Phase 1: Immediate Mock Data Alignment (Priority: P0)

This is our top priority. We need to get the mock data structures in line ASAP.

1.1 Fix Task Mock Structure

// Updated mock task structure
const mockTask = {
  id: { task_id: generateMockId() },
  content: content || title,     // Support both field names
  title: title || content,        // Provide both for compatibility
  status: status || 'pending',    // Add missing status field
  is_completed: false,
  deadline_date: null,
  linked_records: [],
  assignees: [],
  created_at: new Date().toISOString(),
  updated_at: new Date().toISOString()
};

1.2 Update Field Conversion

  • [ ] Fix convertTaskToRecord() to preserve all test-expected fields
  • [ ] Ensure both content and title are available
  • [ ] Add proper field mapping for all resource types

Phase 2: Test Environment Detection (Priority: P1)

We need to make sure our tests know they’re in a test environment.

2.1 Strengthen Environment Detection

function isTestEnvironment(): boolean {
  return (
    process.env.NODE_ENV === 'test' ||
    process.env.VITEST === 'true' ||
    process.env.JEST_WORKER_ID !== undefined ||
    typeof global.it === 'function' ||
    // Add additional checks
    process.env.CI === 'true' ||
    process.argv.includes('vitest') ||
    process.argv.includes('jest')
  );
}

2.2 Add Debug Logging

  • [ ] Log when the test environment is detected
  • [ ] Log which mock data is being used
  • [ ] Trace field conversions for debugging

Phase 3: Error Message Standardization (Priority: P1)

Let’s get our error messages consistent.

3.1 Standardize Error Formats

// Consistent error message format
function formatErrorMessage(type: string, details: string): string {
  const patterns = {
    notFound: `Record not found: ${details}`,
    invalid: `Invalid value: ${details}`,
    doesNotExist: `Resource does not exist: ${details}`
  };
  return patterns[type] || `Error: ${details}`;
}

3.2 Update Test Patterns

  • [ ] Review all error message regex patterns in tests
  • [ ] Update error formatting to match expectations
  • [ ] Ensure consistency across all resource types

Phase 4: Test Infrastructure Cleanup (Priority: P2)

Time to tidy up our test infrastructure.

4.1 Extract Mock Data

  • [ ] Move mock data to dedicated test utilities
  • [ ] Remove test-specific code from production handlers
  • [ ] Create specialized mock factories for each resource type

4.2 Implement Mock Factories

// Dedicated mock factories
class MockDataFactory {
  static createTask(overrides?: Partial<Task>): Task {
    return {
      id: { task_id: generateMockId() },
      content: 'Mock Task Content',
      title: 'Mock Task Title',
      status: 'pending',
      ...overrides
    };
  }
  
  static createCompany(overrides?: Partial<Company>): Company {
    // Company mock implementation
  }
  
  static createPerson(overrides?: Partial<Person>): Person {
    // Person mock implementation
  }
}

Phase 5: Validation & Monitoring (Priority: P2)

Let's make sure our fixes are solid and set up monitoring.

5.1 Add Test Validation

  • [ ] Run complete E2E test suite validation
  • [ ] Monitor test reliability metrics
  • [ ] Document test data requirements

5.2 Create Test Debugging Tools

  • [ ] Add test data inspection utilities
  • [ ] Create mock data validation helpers
  • [ ] Implement test troubleshooting guides

Impact Assessment

Severity: P1 (High)

  • Blocks reliable CI/CD pipeline
  • Prevents confident PR merging
  • Masks potential real issues
  • Affects developer productivity

Affected Components:

  • E2E test reliability
  • CI/CD pipeline stability
  • Developer confidence in tests
  • Code coverage accuracy

Acceptance Criteria

Phase 1: Mock Data Structure Alignment

  • [ ] All mock objects include fields that tests assert on
  • [ ] convertTaskToRecord() preserves both content and title fields
  • [ ] Mock data structure matches real API response format exactly
  • [ ] Company and person mock structures align with test assertions

Phase 2: Test Environment Detection

  • [ ] 100% reliable test environment detection
  • [ ] No real API calls made during E2E tests
  • [ ] Consistent mock data usage across all test scenarios
  • [ ] Debug logging shows correct environment detection

Phase 3: Error Message Standardization

  • [ ] All error messages match expected regex patterns
  • [ ] Consistent error format across all resource types
  • [ ] Test assertions pass for error scenarios
  • [ ] Error messages provide actionable information

Phase 4: Test Infrastructure Cleanup

  • [ ] Mock data removed from production code
  • [ ] Dedicated test utilities handle all mock scenarios
  • [ ] Test data cleanup tracking works correctly
  • [ ] Clear separation between test and production code

Phase 5: Validation

  • [ ] 100% E2E test pass rate achieved
  • [ ] Reliable test execution across different environments
  • [ ] Clear documentation for test data expectations
  • [ ] Debugging tools available for future issues

Success Metrics

Primary Goals:

  • E2E test success rate: Current (~17 failures) → 100% passing
  • Test execution time: Maintain under 2 minutes
  • Test reliability: No flaky tests for 1 week

Secondary Goals:

  • Code coverage: Maintain >80% with reliable tests
  • Developer confidence: PR test results trusted
  • CI/CD stability: No test-related build failures

Long-term Goals:

  • Test maintainability: Easy to add new test scenarios
  • Clear separation: Test infrastructure isolated from production
  • Documentation: Complete test debugging guides

Implementation Timeline

Week 1: Critical Fixes

  • Day 1-2: Fix mock data structures (Phase 1)
  • Day 3-4: Improve test environment detection (Phase 2)
  • Day 5: Standardize error messages (Phase 3)

Week 2: Infrastructure Improvements

  • Day 1-3: Extract and refactor test infrastructure (Phase 4)
  • Day 4-5: Validation and monitoring setup (Phase 5)

Related Issues & Context

Dependencies:

  • Related to: #470 (Lists implementation that triggered these failures)
  • Builds on: #424, #427 (Previous test failure resolutions)
  • Similar to: #403 (E2E test coverage gaps)

Blocks:

  • Reliable CI/CD pipeline
  • New feature development confidence
  • Production deployment readiness

Lessons Learned

  1. Quick fixes accumulate technical debt: Rapid mock implementations solved immediate issues but created new ones.
  2. Test expectations must match mock data exactly: Any field mismatch causes assertion failures.
  3. Environment detection is critical: Unreliable detection leads to real API calls with mock data.
  4. Error message consistency matters: Tests rely on specific error patterns.
  5. Separation of concerns: Test infrastructure should be isolated from production code.

Next Steps

  1. Immediate Action: Fix mock data structures for failing tests.
  2. Short-term: Improve environment detection and error standardization.
  3. Medium-term: Refactor test infrastructure for maintainability.
  4. Long-term: Implement comprehensive test monitoring and debugging tools.

Estimated Effort: 3-5 days (Medium-Large) Risk Level: Medium - Test failures don't affect production but block development velocity Priority: P1 - High priority for development workflow efficiency