AIDB Testing Strategy

Priority: E2E → Integration → Unit (Highest ROI First)

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CRITICAL: Test Execution Command

All tests MUST be run via `./dev-cli test run`:

./dev-cli test run -s {suite} [-k 'pattern'] [-l {lang}]

• Multiple -k and -l flags supported
• NEVER use `--local` - suites know their natural execution environment; forcing local causes unexpected behavior
• Direct pytest invocation is NOT supported

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This skill guides you through creating and modifying tests for the AIDB project. The test infrastructure is complete - your job is to implement tests using proven patterns.

Related Skills

When implementing tests, you may also need:

• adapter-development - Tests validate adapter behavior across languages
• mcp-tools-development - MCP tools tested with DebugInterface abstraction
• dap-protocol-guide - Tests exercise DAP protocol flows end-to-end
• ci-cd-workflows - For testing CI/CD workflows themselves (not application code)

Core Philosophy

For complete test architecture, see test infrastructure in src/tests/.

1. E2E First, Unit Last

Why E2E First?

• Validates real user workflows
• Exercises the full stack (catches integration bugs early)
• Most bugs discovered when testing actual components
• Higher ROI than unit tests initially

Testing Priority:

1. E2E Tests - Complete workflows, real programs, full integration
2. Integration Tests - Component interactions, adapter lifecycle, state management
3. Unit Tests - Edge cases, specific logic, error handling

2. Framework Tests: Keep It Simple

Goal: Verify we can launch and debug programs using various frameworks

Pattern:

1. Connect to framework app (Django, Express, Spring Boot, etc.)
2. Quick smoke test: set breakpoint → inspect → step
3. That's it. Don't test framework internals.

We're testing that AIDB works WITH frameworks, not testing the frameworks themselves.

3. MCP Responses: Structure + Content + Efficiency

Don't just validate structure - validate content accuracy and payload efficiency.

Bad test:

assert "locals" in response["data"]  # Structure only

Good test:

# Structure
assert "locals" in response["data"]

# Content accuracy
assert response["data"]["locals"]["x"]["value"] == 10
assert response["data"]["locals"]["x"]["line"] == 5

# Efficiency (no junk)
assert len(response["data"]) <= 3  # No bloated payloads
assert len(response["summary"]) <= 200  # Concise summaries

4. VS Launch: Critical for Agent Workflows

Why critical:

• Primary entry point for agents using framework debugging
• Enables use of existing workspace launch configs
• Complex variable substitution must work (${workspaceFolder}, etc.)

Test thoroughly:

• Core launch.json parsing (language-independent)
• Per-language config translation (Python/JavaScript/Java)
• Variable substitution and resolution
• Framework-specific launch configs

Critical Breakpoint Timing:

Set breakpoints when STARTING sessions (not after) to avoid race conditions with fast-executing programs:

# ✅ CORRECT
await debug_interface.start_session(program=prog, breakpoints=[{"line": 10}])

# ❌ WRONG: Race condition
await debug_interface.start_session(program=prog)
await debug_interface.set_breakpoint(file=prog, line=10)  # May be too late!

Exception: Long-running processes (servers) where you attach. Reference: src/tests/aidb_shared/e2e/test_complex_workflows.py

DebugInterface Abstraction

The cornerstone of our test strategy is the DebugInterface abstraction - a unified API that works with both MCP tools and the direct API.

For implementation details, see:

• DebugInterface - Skill resource file
• src/tests/_helpers/debug_interface/ - Debug interface source and docstrings

Why? One test validates both entry points.

Hypothetical Example (illustrates pattern, not a real test file):

from tests._helpers.parametrization import parametrize_interfaces

class TestBreakpoints(BaseE2ETest):
    @parametrize_interfaces  # Runs twice: MCP and API
    @pytest.mark.asyncio
    async def test_set_breakpoint(self, debug_interface, simple_program):
        """Test works with BOTH MCP and API."""
        await debug_interface.start_session(program=simple_program)

        bp = await debug_interface.set_breakpoint(
            file=simple_program,
            line=5
        )

        self.verify_bp.verify_breakpoint_verified(bp)
        await debug_interface.stop_session()

Key Points:

• @parametrize_interfaces runs test with both MCP and API
• Same test logic validates both entry points
• No duplication, no drift
• See DebugInterface for details

The Shared Suite: Testing Debug Fundamentals

The shared suite is AIDB's language-agnostic test foundation that validates core debugging capabilities across all supported languages using normalized, programmatically generated test programs.

Key Innovation: Semantic markers that map identical logic to language-specific line numbers.

Location: src/tests/aidb_shared/ (integration/ + e2e/)

What it tests:

• Debug primitives (breakpoints, stepping, variables)
• Control flow across all 3 languages (Python, JavaScript, Java)
• Zero duplication: One test → 6 execution paths (2 interfaces × 3 languages)

For complete details, see DebugInterface.

When to Use the Shared Suite

Use shared suite when:

• Testing core debug operations (breakpoint, step, inspect)
• Validating adapter behavior across languages
• Ensuring language parity (all adapters work identically)

Use framework tests when:

• Testing framework-specific debugging (Django ORM, Express middleware)
• Validating launch.json configurations
• Testing real-world application patterns

Test Organization

src/tests/
├── aidb_shared/               # ⭐ Shared suite: language-agnostic debug fundamentals
│   ├── integration/          # Core debug operations (breakpoints, stepping, variables)
│   └── e2e/                  # Complex workflows, parallel sessions
├── aidb/                      # Core API tests - organized by component
│   ├── adapters/             # Adapter-specific tests
│   ├── audit/                # Audit logging tests
│   ├── common/               # Common utilities tests
│   ├── dap/                  # DAP client tests
│   ├── models/               # Model tests
│   ├── resources/            # Resource management tests
│   ├── service/              # Service layer tests
│   └── session/              # Session management tests
├── aidb_mcp/                  # MCP server tests - organized by component
├── frameworks/                # Framework integration tests
│   ├── python/               # Flask, FastAPI, pytest
│   ├── javascript/           # Express, Jest
│   └── java/                 # Spring Boot, JUnit
├── _helpers/                  # Test helpers and utilities
├── _fixtures/                 # Shared fixtures
│   └── unit/                 # ⭐ Unit test infrastructure (see below)
└── _assets/                   # Test programs and data
    ├── framework_apps/       # Framework test applications
    └── test_programs/        # Generated programs for shared suite

Unit Test Infrastructure

The centralized unit test infrastructure at src/tests/_fixtures/unit/ provides reusable mocks, builders, and fixtures:

_fixtures/unit/
├── builders/           # DAPRequestBuilder, DAPResponseBuilder, DAPEventBuilder
├── dap/               # Transport, events, receiver mocks
├── session/           # Registry, lifecycle, state, child_manager mocks
├── adapter/           # Port, process, launch_orchestrator mocks
├── mcp/               # DebugService, MCPSessionContext mocks
├── conftest.py        # Master fixture re-exports
├── context.py         # mock_ctx, null_ctx, tmp_storage
└── assertions.py      # UnitAssertions class

Usage Pattern:

# In domain conftest.py (e.g., src/tests/aidb/dap/unit/conftest.py)
from tests._fixtures.unit.conftest import *  # noqa: F401, F403
from tests._fixtures.unit.builders import DAPEventBuilder, DAPResponseBuilder

# In test file
def test_something(mock_ctx, mock_transport):
    event = DAPEventBuilder.stopped_event(reason="breakpoint")
    # ...

Key Components:

• Builders - Fluent API for DAP protocol objects (requests, responses, events)
• mock_ctx - Standard logging context mock with debug/info/warning/error methods
• UnitAssertions - DAP-specific assertion helpers

Test Execution Modes

Test suites run in different environments based on their requirements:

Local-Only Suites (no Docker):

• cli - CLI command tests
• mcp - MCP server unit/integration tests
• core - Core AIDB API tests
• common - Common utilities tests
• logging - Logging framework tests
• ci_cd - CI/CD workflow tests

Docker Suites (require containers):

• shared - Multi-language shared tests (parallel language containers)
• frameworks - Framework integration tests (parallel language containers)
• launch - Launch config tests (parallel language containers)

Why the split?

• Local suites test Python-only logic (handlers, validation, utils)
• Docker suites test multi-language scenarios
• Multi-language MCP functionality tested in shared/frameworks/launch

Running tests:

./dev-cli test run -s mcp      # Local execution
./dev-cli test run -s shared   # Docker execution

Code Reuse: Don't Reinvent

Always use existing infrastructure:

• Test Base Classes - BaseE2ETest, BaseIntegrationTest, FrameworkDebugTestBase
• Parametrization Decorators - @parametrize_interfaces, @parametrize_languages
• Helper Assertions - self.verify_bp, self.verify_exec, MCPAssertions
• Constants - StopReason, TestTimeouts, MCPTool

For complete details, see E2E Patterns.

Working Examples

Study these real tests before writing new ones:

Framework Tests (E2E)

• Python: test_flask_debugging.py, test_fastapi_debugging.py, test_pytest_debugging.py
• JavaScript: test_express_debugging.py, test_jest_debugging.py
• Java: test_springboot_debugging.py, test_junit_debugging.py

Core API Tests

• Launch Variable Resolution: test_launch_variable_resolution.py
• Session Target Handling: test_session_target_handling.py

For complete file paths and patterns, see E2E Patterns.

Common Patterns

For hypothetical examples illustrating common patterns, see E2E Patterns.

Key patterns covered:

1. Basic E2E Test
2. Breakpoint Test with Markers
3. Dual-Launch Equivalence Test
4. MCP Response Validation

When Creating New Tests

Step 1: Choose Test Type

• E2E? Full workflow, real program, complete integration
• Integration? Component interactions, lifecycle management
• Unit? Specific function, edge case, error handling

Step 2: Find Similar Test

Look at E2E Patterns:

• Django/Express for framework tests
• Existing tests in the same directory
• Similar test scenarios in other languages

Step 3: Copy Pattern, Adapt

Don't start from scratch:

1. Copy a working test
2. Adapt to your scenario
3. Use same helpers and assertions
4. Follow same structure

Step 4: Use Existing Infrastructure

Don't create:

• New assertion helpers (use existing)
• New fixtures (check conftest.py files first)
• New constants (use constants.py)
• New base classes (inherit from existing)

Do create:

• Tests using existing patterns
• Scenario-specific test data
• Framework-specific fixtures (if needed)

Performance Testing

Current State: No performance baselines exist yet

Phase 1: Establish baselines

• Analyze existing metrics/instrumentation
• Determine "healthy" latencies
• Document target times

Phase 2: Regression testing

• Monitor key operations (breakpoint set, variable inspect, step)
• Alert on degradation

For now: Focus on functional correctness, not performance.

Success Criteria

Test Quality Checklist

• [ ] Test uses @parametrize_interfaces for MCP/API coverage
• [ ] Test inherits from appropriate base class
• [ ] Test uses helper assertions, not custom assertions
• [ ] Test validates content accuracy, not just structure
• [ ] MCP tests check efficiency (no bloated payloads)
• [ ] Test has clear docstring explaining what it validates
• [ ] Test follows working examples
• [ ] Test is in correct directory (e2e/integration/unit)

Framework Test Checklist

• [ ] Inherits from FrameworkDebugTestBase
• [ ] Implements test_launch_via_api()
• [ ] Implements test_launch_via_vscode_config()
• [ ] Implements test_dual_launch_equivalence()
• [ ] Sets framework_name attribute
• [ ] Uses simple smoke tests (no deep framework testing)

Investigating Test Failures

CRITICAL: When tests fail, check logs BEFORE attempting fixes.

See: [Debugging Failures](resources/debugging-failures.md) for log locations, investigation workflow, and common patterns.

For CI test failures, use the ci-cd-workflows skill's troubleshooting guide.

Resources

Test Infrastructure: src/tests/ (see \_fixtures/, \_helpers/ for core components)

Getting Started

1. Read CONTEXT.md: wip/test-implementation-backlog/CONTEXT.md
2. Study working examples: Flask (test_flask_debugging.py) and Express (test_express_debugging.py)
3. Choose a test to implement: Start with E2E (highest ROI)
4. Copy a working test: Don't start from scratch
5. Adapt to your scenario: Use same patterns, different data
6. Validate: Run test, ensure it passes with both MCP and API

Questions?

Internal Documentation:

• src/tests/ - Test infrastructure (see \_fixtures/, \_helpers/)
• docs/developer-guide/overview.md - System architecture

Code References:

• DAP Protocol: See src/aidb/dap/protocol/ (fully typed, types.py + requests.py + responses.py + events.py)
• Test Infrastructure: See src/tests/_helpers/ and src/tests/_fixtures/
• Working Examples: See Flask/Express framework tests

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Remember:

• E2E first, validate content accuracy
• Use shared suite for debug fundamentals, framework tests for integration
• Keep framework tests simple (no framework internals)
• Always use the DebugInterface abstraction (zero duplication)