The Autonomous Deterministic Quality Loop
BitDive records reality, guides a precise fix, proves the outcome, and turns the result into reusable regression memory.
Next baseline
Capture Real Behavior
Capture real runtime data: traces, SQL, method calls, request payloads, and downstream interactions to establish the current baseline.
Make One Focused Change
Use the real execution context to fix the issue precisely instead of guessing through broad refactors or synthetic tests.
Compare Before and After
Run before/after trace comparison to catch behavior drift, extra SQL, performance regressions, and hidden side effects.
Refresh Deterministic Tests
Create deterministic JUnit replay tests with zero token usage from real runtime data, not AI-invented guesses.
On-Prem BitDive in one terminal command
docker run -d --privileged -p 443:443 -p 8089:8089 --name bitdive-launcher frolikoveabitdive/bitdive-launcherdocker logs -f bitdive-launcher# ➔ https://localhost
# Default credentials: firstUser / 111111 (first-time login reset required)
# Need help? Drop us a line: support@bitdive.io
npx skills add bitDive/bitdive-skills --skill '*'One Recording, Full Execution Context
A single runtime snapshot becomes the baseline for debugging, AI reasoning, and deterministic regression replay.
Instead of reconstructing state from logs, BitDive captures the real execution surface that matters to verification.
- HTTP request payloads and headers
- Execution tree with timings
- Method arguments and return values
- Database queries with results
- REST requests and responses
- Kafka publishes and consumed messages
- Exception details and failure paths
Deterministic Replay Tests, Not Synthetic AI Test Code
Real executions become standard JUnit replay tests with virtualized boundaries and zero manual mock setup.
BitDive does not ask an LLM to invent tests. It records what the application actually did and replays that behavior as runnable regression assets.
- Runtime-grounded: replay suites are built from real application behavior, not imagined scenarios.
- Boundary virtualization: databases, REST calls, and Kafka interactions are isolated directly in the JVM.
- Standard output: recorded suites remain ordinary JUnit that runs via
mvn test.
How BitDive Fits into the AI Development Loop
The agent does not jump straight from prompt to patch. It moves through baseline, change, proof, and regression management.
Prep and Behavioral Baseline
Before changing code, the agent studies the current system state and understands how it behaves in reality.
- Identify the relevant module, service, and execution path.
- Run the current test suite to document the starting state.
- Inspect the before-trace to understand internal calls, SQL, timing, and business logic.
Precise Code Change
Implementation is grounded in observed runtime data instead of assumptions about the code path.
- Use captured inputs, outputs, and dependencies to scope the change.
- Prefer a small fix over a wide refactor when the trace isolates the issue.
- Validate behavior internally, not only via the top-level HTTP response.
Verification and Reflection
The agent proves the fix by comparing runtime behavior before and after the modification.
- Trace comparison becomes the main evidence for correctness.
- Spot N+1 queries, unnecessary downstream calls, or latency regressions.
- Run standard regression checks to confirm the wider system still holds.
Regression Management
The resulting behavior is turned into reusable JUnit regression plans so the system keeps its memory.
- Create or refresh replay suites from the newest successful executions.
- Keep tests aligned with real business behavior rather than synthetic assumptions.
- Update only what changed instead of rewriting entire test suites.
Where BitDive Saves Resources
Strategic resource recovery across the entire development lifecycle, from AI token consumption to human engineering hours.
- Zero token usage to create deterministic tests from captured executions
- Nearly zero token usage to refresh tests after a verified change
- No AI-written test logic to debug or rewrite
- Fewer AI agent iterations due to precise runtime context
- Less mock work because dependencies are auto-mocked from reality
- Reduced cloud costs via efficient code (no N+1, no duplicate requests)
- Less time preparing context for AI tools
- Test suites created in minutes instead of by hand
- Significantly faster root-cause analysis
- Less manual mock setup with automatic dependency virtualization
- Fewer production incidents through deterministic verification
- Better performance visibility for regressions, redundant calls, and N+1
What Makes This Work in Real Systems
Runtime capture, trace comparison, and replay tests only matter if they stay fast, stable, and safe under production constraints.
Rapid Integration
Deploy the full platform in minutes via a single Docker Compose or use the SaaS option. Connect your app by adding the BitDive dependency. No code changes or manual routing setup.
Production Performance
Designed for high-load environments with low overhead (0.5-5% CPU depending on workload). Uses custom binary format for event capture, compression, and serialization to minimize impact.
Noise Reduction
Automatically ignores noisy fields (UUIDs, timestamps, binary payloads) during comparison to prevent false positives. Prevents false positives and keeps the regression barrier stable across runs.
PII Masking
Protect sensitive data with configurable masking rules. Automatically scrubs PII (email, card numbers) before captured data leaves memory.
Zero Trust Security
Zero Trust architecture with encryption in transit and at rest. Every component is authenticated and access controlled at the network layer.
Auto Mocking & Virtualization
Run complex business flows without infrastructure. Execute thousands of integration-level scenarios in seconds with unit test speed.
Build the Verification Layer Your AI Agent Is Missing
Ground every change in runtime evidence, prove it with trace comparison, and keep the result as deterministic regression memory.
