11: Synthetic Data and Digital Twins
Safe Experimentation at Scale
Pillar: #11 of 12 - Supporting Priority
Timeline: 6 Weeks
Dependencies: #08 (Observability)
Expected ROI: 100x test scenario coverage, risk-free experimentation, edge case discovery
Executive Summary
Testing AI agents in production is risky and expensive. This pillar creates digital twins of production environments and synthetic data generators that produce realistic, diverse test scenarios. This enables comprehensive testing without touching production, discovering edge cases before users do.
Key Insight: The best way to find bugs is to generate scenarios you'd never think of.
Problem Statement
Current State: Limited Test Coverage
# File: tests/test_workflow.py
# Current: Manual test case creation
def test_workflow_basic():
"""One happy path test."""
workflow = create_workflow(
requirement="Build a simple API", # Same test every time
)
result = await execute(workflow)
assert result.success
# Problems:
# - Limited scenarios
# - No edge cases
# - No stress testing
# - No rare condition testing
Testing Gaps
Current Coverage:
┌──────────────────────────────────────────────┐
│ ████████████░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░ │
│ 20% 80% UNTESTED │
└──────────────────────────────────────────────┘
Missing:
- Complex multi-step workflows
- Concurrent execution scenarios
- Error recovery paths
- Edge case inputs
- Performance under load
- Rare but critical scenarios
Why Real Data Is Insufficient
Real Production Data:
✗ Privacy concerns (PII, PHI)
✗ Limited diversity (common cases only)
✗ Can't test rare scenarios
✗ Can't inject failures
✗ Snapshot in time (stale)
Synthetic Data:
✓ No privacy concerns
✓ Controllable diversity
✓ Any scenario possible
✓ Failure injection
✓ Always fresh
Solution Architecture
┌────────────────────────────────────────────┐
│ DIGITAL TWIN PLATFORM │
├────────────────────────────────────────────┤
│ │
Production ────────►│ Snapshot Engine │
│ ├── Schema extraction │
│ ├── Anonymization │
│ └── State capture │
│ │
│ Twin Environment │
│ ├── Isolated replica │
│ ├── Synthetic data │
│ └── Failure injection │
│ │
│ Scenario Generator │
│ ├── Requirement synthesis │
│ ├── Edge case generation │
│ └── Adversarial inputs │
│ │
│ Experiment Runner │
│ ├── A/B testing │
│ ├── Chaos engineering │
│ └── Performance testing │
│ │
└────────────────────────────────────────────┘
Core Components
1. Synthetic Requirement Generator
class SyntheticRequirementGenerator:
"""
Generate diverse, realistic requirements for testing.
"""
def __init__(self):
self.llm = LLMClient()
self.templates = self._load_templates()
self.domain_vocabulary = self._load_vocabulary()
async def generate(
self,
count: int,
config: GenerationConfig
) -> List[SyntheticRequirement]:
requirements = []
for _ in range(count):
# Select generation strategy
if config.strategy == "template":
req = await self._generate_from_template(config)
elif config.strategy == "llm":
req = await self._generate_from_llm(config)
elif config.strategy == "mutation":
req = await self._generate_from_mutation(config)
else:
req = await self._generate_hybrid(config)
# Apply complexity modifiers
req = self._apply_complexity(req, config.complexity)
# Add edge case elements
if config.include_edge_cases:
req = self._inject_edge_cases(req)
requirements.append(req)
return requirements
async def _generate_from_llm(
self,
config: GenerationConfig
) -> SyntheticRequirement:
"""Use LLM to generate novel requirements."""
prompt = f"""
Generate a realistic software requirement for a {config.domain} application.
Complexity: {config.complexity}
Type: {config.requirement_type}
Include: {config.features}
Constraints: {config.constraints}
Format as a detailed requirement specification.
"""
response = await self.llm.generate(prompt)
return SyntheticRequirement(
content=response.content,
complexity=config.complexity,
domain=config.domain,
features=self._extract_features(response.content),
)
async def _generate_from_mutation(
self,
config: GenerationConfig
) -> SyntheticRequirement:
"""Mutate existing requirements to create variations."""
base = random.choice(self.templates)
mutations = [
self._add_feature,
self._remove_feature,
self._change_constraint,
self._increase_complexity,
self._add_integration,
]
mutated = base
for _ in range(config.mutation_count):
mutation = random.choice(mutations)
mutated = mutation(mutated)
return mutated
def _inject_edge_cases(
self,
req: SyntheticRequirement
) -> SyntheticRequirement:
"""Add edge case elements to requirement."""
edge_cases = [
"Must handle Unicode in all text fields",
"Must work with empty inputs",
"Must handle concurrent access",
"Must timeout after 30 seconds",
"Must handle network failures gracefully",
"Must work with very large inputs (>1MB)",
"Must handle special characters",
]
req.content += "\n\nEdge Cases:\n"
for case in random.sample(edge_cases, min(3, len(edge_cases))):
req.content += f"- {case}\n"
return req
2. Data Synthesizer
class DataSynthesizer:
"""
Generate realistic synthetic data for testing.
"""
def __init__(self):
self.faker = Faker()
self.generators = self._init_generators()
def generate_dataset(
self,
schema: DataSchema,
count: int,
config: SynthesisConfig
) -> Dataset:
"""Generate a synthetic dataset matching schema."""
records = []
for _ in range(count):
record = {}
for field in schema.fields:
generator = self.generators.get(field.type)
value = generator(field, config)
# Apply constraints
if field.constraints:
value = self._apply_constraints(value, field.constraints)
record[field.name] = value
# Apply cross-field dependencies
if schema.dependencies:
record = self._apply_dependencies(record, schema.dependencies)
records.append(record)
return Dataset(
records=records,
schema=schema,
metadata={
"generated_at": now(),
"generator_version": "1.0",
"config": config.to_dict(),
}
)
def _init_generators(self) -> Dict[str, Callable]:
return {
"string": self._generate_string,
"email": lambda f, c: self.faker.email(),
"name": lambda f, c: self.faker.name(),
"address": lambda f, c: self.faker.address(),
"phone": lambda f, c: self.faker.phone_number(),
"date": lambda f, c: self.faker.date_between(
start_date="-5y", end_date="today"
),
"datetime": lambda f, c: self.faker.date_time_between(
start_date="-5y", end_date="now"
),
"integer": lambda f, c: random.randint(
f.min_value or 0, f.max_value or 1000000
),
"float": lambda f, c: random.uniform(
f.min_value or 0.0, f.max_value or 1000000.0
),
"boolean": lambda f, c: random.choice([True, False]),
"uuid": lambda f, c: str(uuid.uuid4()),
"json": self._generate_json,
"code": self._generate_code,
}
def _generate_code(self, field: Field, config: SynthesisConfig) -> str:
"""Generate synthetic code samples."""
templates = {
"python": [
"def {name}({params}):\n return {expr}",
"class {name}:\n def __init__(self):\n self.{attr} = {value}",
],
"javascript": [
"function {name}({params}) {{\n return {expr};\n}}",
"const {name} = ({params}) => {expr};",
],
}
lang = field.language or "python"
template = random.choice(templates[lang])
return template.format(
name=self.faker.word(),
params=", ".join(self.faker.words(random.randint(0, 3))),
expr=self._generate_expression(lang),
attr=self.faker.word(),
value=random.choice(["None", "0", "[]", "{}"]),
)
3. Digital Twin Manager
class DigitalTwinManager:
"""
Create and manage digital twins of production environments.
"""
async def create_twin(
self,
source: Environment,
config: TwinConfig
) -> DigitalTwin:
"""Create a digital twin of an environment."""
# Capture schema
schema = await self._capture_schema(source)
# Create isolated environment
twin_env = await self._provision_environment(config)
# Generate synthetic data based on schema
synthetic_data = await self._generate_data(
schema=schema,
scale=config.data_scale,
anonymize=True,
)
# Load data into twin
await self._load_data(twin_env, synthetic_data)
# Configure services
for service in source.services:
await self._configure_service(twin_env, service, config)
return DigitalTwin(
id=generate_id(),
source=source.id,
environment=twin_env,
schema=schema,
created_at=now(),
config=config,
)
async def sync_twin(
self,
twin: DigitalTwin,
sync_type: str = "schema"
):
"""Sync twin with source environment."""
if sync_type == "schema":
# Update schema only
new_schema = await self._capture_schema(twin.source)
await self._update_schema(twin, new_schema)
elif sync_type == "data":
# Regenerate synthetic data
synthetic_data = await self._generate_data(
schema=twin.schema,
scale=twin.config.data_scale,
)
await self._reload_data(twin, synthetic_data)
async def inject_failure(
self,
twin: DigitalTwin,
failure: FailureInjection
):
"""Inject failure conditions for testing."""
if failure.type == "network":
await self._inject_network_failure(twin, failure)
elif failure.type == "latency":
await self._inject_latency(twin, failure)
elif failure.type == "error":
await self._inject_error(twin, failure)
elif failure.type == "resource":
await self._inject_resource_exhaustion(twin, failure)
4. Scenario Runner
class ScenarioRunner:
"""
Run test scenarios against digital twins.
"""
async def run_scenario(
self,
scenario: Scenario,
twin: DigitalTwin
) -> ScenarioResult:
"""Execute a test scenario."""
results = []
for step in scenario.steps:
# Set up step conditions
if step.preconditions:
await self._setup_preconditions(twin, step.preconditions)
# Execute step
step_result = await self._execute_step(twin, step)
results.append(step_result)
# Check assertions
if step.assertions:
assertion_results = await self._check_assertions(
twin, step.assertions, step_result
)
if not all(a.passed for a in assertion_results):
return ScenarioResult(
passed=False,
steps=results,
failure_step=step,
assertions=assertion_results,
)
return ScenarioResult(
passed=True,
steps=results,
metrics=self._collect_metrics(results),
)
async def run_scenario_suite(
self,
suite: ScenarioSuite,
twin: DigitalTwin,
parallelism: int = 10
) -> SuiteResult:
"""Run a suite of scenarios in parallel."""
semaphore = asyncio.Semaphore(parallelism)
async def run_with_limit(scenario):
async with semaphore:
return await self.run_scenario(scenario, twin)
results = await asyncio.gather(*[
run_with_limit(s) for s in suite.scenarios
])
return SuiteResult(
total=len(suite.scenarios),
passed=sum(1 for r in results if r.passed),
failed=sum(1 for r in results if not r.passed),
results=results,
)
async def run_chaos_test(
self,
twin: DigitalTwin,
duration: timedelta,
config: ChaosConfig
) -> ChaosResult:
"""Run chaos engineering tests."""
events = []
deadline = now() + duration
# Start workload
workload_task = asyncio.create_task(
self._run_workload(twin, config.workload)
)
while now() < deadline:
# Inject random failure
failure = self._select_failure(config.failure_types)
await self.twin_manager.inject_failure(twin, failure)
events.append(ChaosEvent(
type=failure.type,
timestamp=now(),
))
# Observe recovery
recovery_time = await self._measure_recovery(twin)
events[-1].recovery_time = recovery_time
# Wait before next injection
await asyncio.sleep(config.injection_interval.total_seconds())
workload_task.cancel()
return ChaosResult(
events=events,
avg_recovery_time=sum(e.recovery_time for e in events) / len(events),
failures_survived=len([e for e in events if e.recovered]),
)
5. Edge Case Discovery
class EdgeCaseDiscoverer:
"""
Automatically discover edge cases through exploration.
"""
async def discover(
self,
function: Callable,
config: DiscoveryConfig
) -> List[EdgeCase]:
"""Discover edge cases through guided exploration."""
edge_cases = []
coverage = CoverageTracker()
# Initial random exploration
for _ in range(config.initial_samples):
input_data = self._generate_random_input(function)
result = await self._execute_and_track(function, input_data, coverage)
if self._is_interesting(result):
edge_cases.append(EdgeCase(input_data, result))
# Coverage-guided exploration
for _ in range(config.guided_samples):
# Generate input that targets uncovered code
input_data = self._generate_guided_input(function, coverage)
result = await self._execute_and_track(function, input_data, coverage)
if self._is_interesting(result):
edge_cases.append(EdgeCase(input_data, result))
# Mutation-based exploration
for edge in edge_cases[:config.mutation_seeds]:
mutations = self._generate_mutations(edge.input)
for mutated in mutations:
result = await self._execute_and_track(function, mutated, coverage)
if self._is_interesting(result):
edge_cases.append(EdgeCase(mutated, result))
return edge_cases
def _is_interesting(self, result: ExecutionResult) -> bool:
"""Check if a result reveals an edge case."""
return (
result.exception is not None or
result.timeout or
result.memory_exceeded or
result.output_size > 1_000_000 or
result.execution_time > 10.0 or
self._is_unexpected_output(result)
)
async def generate_test_suite(
self,
edge_cases: List[EdgeCase]
) -> TestSuite:
"""Generate test suite from discovered edge cases."""
tests = []
for edge in edge_cases:
test = TestCase(
name=f"test_edge_case_{edge.id}",
input=edge.input,
expected_behavior=self._classify_behavior(edge),
assertions=self._generate_assertions(edge),
)
tests.append(test)
return TestSuite(tests=tests)
Integration Points
Files to Modify
CI Integration
# .github/workflows/synthetic-tests.yml
jobs:
synthetic-tests:
runs-on: ubuntu-latest
steps:
- name: Create Digital Twin
run: python -m twins create --source=staging --scale=0.1
- name: Generate Scenarios
run: python -m synthetic generate-scenarios --count=1000
- name: Run Scenario Suite
run: python -m twins run-suite --scenarios=generated/
- name: Run Chaos Tests
run: python -m twins chaos --duration=30m
- name: Collect Metrics
run: python -m twins report --output=results/
Implementation Plan
Week 1: Requirement Generator
- [ ] Build template-based generator
- [ ] Add LLM-based generation
- [ ] Implement mutation strategies
- [ ] Create edge case injection
Week 2: Data Synthesizer
- [ ] Implement core generators
- [ ] Add constraint support
- [ ] Build cross-field dependencies
- [ ] Create code generators
Week 3: Digital Twin Manager
- [ ] Build schema capture
- [ ] Implement environment provisioning
- [ ] Add data loading
- [ ] Create sync mechanism
Week 4: Scenario Runner
- [ ] Build scenario execution
- [ ] Add parallel execution
- [ ] Implement chaos testing
- [ ] Create metrics collection
Week 5: Edge Case Discovery
- [ ] Build random exploration
- [ ] Add coverage-guided generation
- [ ] Implement mutation strategies
- [ ] Create test suite generation
Week 6: Integration
- [ ] CI pipeline integration
- [ ] Dashboard creation
- [ ] Documentation
- [ ] Production rollout
Expected Results
Test Coverage
Bug Discovery
Risks & Mitigations
Success Criteria
- #07 Testing: Synthetic tests feed test suite
- #08 Observability: Metrics from twin experiments
- #06 CI/CD: Synthetic tests in pipeline
Status: Ready for Implementation
Key Insight: The best bugs are the ones you find before users do
Owner: Quality Engineering Team
