Autonomous Verification Layer for AI-Driven Code Changes
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 runtime context to make a precise fix based on method calls, SQL, downstream operations, and hidden failures, not just static code.
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.
Watch the Verification Workflow in Action
Deploy BitDive On-Prem in One Command
docker run -d --privileged -p 443:443 -p 8089:8089 --name bitdive-launcher frolikoveabitdive/bitdive-launchernpx skills add bitDive/bitdive-skills --skill '*'One Runtime Trace, Full Execution Context for Code Verification
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 JUnit Replay Tests from Runtime Traces
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.
Runtime Context and Trace Comparison for AI Agents
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 identifies the target flow and establishes the baseline.
- Identify the exact endpoint, method, or user flow you need to inspect.
- Study the before-trace to understand how the flow works in reality: internal calls, SQL, downstream ops, and failures.
- Run the current replay suite to confirm the starting state.
Precise Code Change
Implementation is grounded in runtime evidence instead of assumptions from static code.
- Use captured inputs, outputs, and dependency behavior to scope the change.
- Prefer a small fix over a wide refactor when the trace isolates the issue.
- Change the logic where the trace shows the behavior actually diverged.
Verification and Reflection
The agent proves the fix by capturing a new trace and comparing it to the baseline.
- Capture a new trace for the same scenario after the change.
- Compare methods, SQL, downstream calls, payloads, and timings before and after.
- Spot N+1 queries, hidden side effects, or latency regressions before shipping.
Regression Management
Once the change is proven, BitDive preserves the verified behavior as reusable JUnit regression tests.
- Create or refresh replay suites from the verified successful execution.
- Keep tests aligned with real business behavior rather than synthetic assumptions.
- Update only what changed instead of rewriting entire test suites.
How Deterministic Verification Reduces Engineering and Testing Costs
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
- 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
Production-Safe Runtime Capture for Java
Fast setup. Low overhead. Stable replay.
Fast Setup
Add the dependency and start capturing. No code changes.
Low Overhead
Built for high-load Java services with binary capture and compression.
Clean Diffs
Ignore UUIDs, timestamps, and payload noise automatically.
PII Masking
Filter and mask sensitive data during capture based on configurable patterns.
Zero-Trust Security
Auth, granular access control, and full encryption for data both at rest and in transit.
Boundary Virtualization
Replay complex flows without booting the whole environment.
Verify Code Changes with Runtime Evidence
Ground every change in runtime evidence, prove it with trace comparison, and keep the result as deterministic regression memory.
