BitDive: The Quality Engine for QA Teams

From manual E2E chaos to stable, reproducible scenarios from real system behavior

Modern QA engineers spend most of their time maintaining fragile automated tests, managing complex mocks, and debugging production issues with incomplete context. BitDive eliminates this gap by transforming real traffic into reliable, automatically verifiable test assets.

Book A Demo

The Reality of QA Today

Modern development moves fast, but test code can't keep up. The fundamental problem: verification doesn't scale with velocity.

🔧🎭🔍⚠️

•Fragile Test Maintenance:

Writing and maintaining brittle automated tests consumes 60-80% of QA time. Every architecture change breaks E2E and integration tests.

•Mock Management Hell:

Creating and updating complex mocks for databases, APIs, and external services is an endless technical debt cycle.

•Long Root Cause Analysis:

When a test fails or production breaks, you can't see WHERE it broke. Hours spent correlating logs across microservices just to find which method caused the error.

•Staging ≠ Production:

Test environments never match production reality, leading to "works in staging" incidents and deployment anxiety.

What QA Teams Say

Real experiences from QA engineers and SDETs using BitDive

"I work as a QA on a project with many Java microservices (Spring Boot, Kafka, several external APIs). We had a floating error for a long time: some user requests just 'disappeared' — sometimes everything worked fine, sometimes the operation hung for no clear reason. In the logs — either a timeout or nothing useful at all. We couldn't reproduce it stably in the test environment. After connecting BitDive, it became easier: I started looking at real call chains from production, not guessing from logs. In one of the traces, we saw that with a certain set of parameters, our service periodically hung on a request to an external API, and then the chain went wrong. We collected this case, reproduced it locally, and found and fixed the problem quite quickly. Now, if some strange, irregular error appears, I immediately go to BitDive to look at the specific traceId and scenario, rather than spending hours manually analyzing logs and trying to 'catch' the bug by hand."

QA Engineer, Java microservices project

6 Critical Jobs BitDive Solves for QA Teams

From stability guarantees to AI-accelerated workflows

01🛡️

Guarantee Product Stability & User Trust

The Problem: Regression testing is manual, slow, and breaks with every architectural change.

BitDive Solution: BitDive captures real scenarios from production/staging and converts them into stable, code-free regression suites that evolve with your system.

02🔗

Ensure Backend and Service Health

The Problem: API and database validation requires manual SQL queries, Postman collections, and log diving across services.

BitDive Solution: BitDive provides complete distributed traces with method-level detail, SQL queries with parameters, and HTTP request/response bodies—all automatically captured.

03🎯

Provide Actionable Defect Intelligence

The Problem: Bug reports lack context, leading to endless back-and-forth between QA and developers to reproduce issues.

BitDive Solution: BitDive UI shows the exact method where the error occurred, with full call chain, parameters, SQL queries, and HTTP responses. No log archaeology—just click the traceId and see everything.

04📈

Scale Automation Without Exploding Tech Debt

The Problem: Test frameworks become complex systems themselves, requiring constant maintenance and slowing down feature delivery.

BitDive Solution: BitDive eliminates test code entirely. Scenarios are captured from real behavior and auto-updated as your system evolves—no framework maintenance.

05✅

Verify Requirements and Design Compliance

The Problem: Tests pass, but there's no confidence that business logic actually matches requirements.

BitDive Solution: BitDive detects semantic drift—changes in actual behavior, SQL patterns, and response data—not just broken selectors or status codes.

06🤖

Accelerate QA with AI Agents

The Problem: AI tools can't help QA because they lack access to real runtime behavior and execution context.

BitDive Solution: BitDive provides machine-readable runtime data via MCP protocol, enabling AI agents to analyze failures, suggest fixes, and verify solutions using actual execution traces.

How It Works: From Traffic to Test in 3 Steps

BitDive isn't another test framework—it's a system that records real behavior and makes it verifiable

Capture: The Truth from Production

Passive, comprehensive runtime recording

BitDive agent runs in your Java/Kotlin services, capturing complete execution context without code changes.

Method calls with parameters and return values
SQL queries with actual parameters and results
HTTP requests and responses (REST, gRPC, Kafka)
Cross-service distributed traces
Exception stack traces with full context

Result: Zero performance impact. Zero code changes. Complete behavioral record.

Replay: Executable Test Scenarios

Auto-mocked, deterministic, isolated

BitDive transforms captured traces into executable test scenarios with automatic dependency isolation.

Auto-generates mocks for databases, APIs, and message queues
Recreates exact execution context from the recording
Runs in any environment (local, CI, staging) without external dependencies
Deterministic execution—same inputs always produce same outputs
No test code to write or maintain

Result: Regression tests that reflect reality, not assumptions.

Verify: Semantic Drift Detection

Behavior validation, not just status checks

BitDive compares execution behavior before and after code changes to detect regressions.

Compares SQL query patterns and results
Validates HTTP response bodies and structures
Detects changes in method call sequences
Identifies performance regressions
Distinguishes bug fixes from breaking changes

Result: Catch regressions in logic, not just broken mocks.

Top 10 QA Responsibilities: Traditional vs. BitDive

Based on analysis of 50+ QA job descriptions and resumes

QA Responsibility	Traditional Approach	With BitDive
Functional Testing	Manual test case execution and maintenance	Behavior-based validation from real scenarios
API Testing (REST/SOAP)	Postman collections requiring constant updates	Full distributed trace with method-level detail
Regression Testing	Brittle E2E scripts that break constantly	Auto-generated from production traffic
Bug Localization & Reporting	Hours correlating logs across microservices	UI shows exact method where error occurred + full context
Database Validation	Manual SQL queries and data checks	Automatic SQL capture with parameters and results
Integration Testing	Complex test environments and mock setup	Isolated replays with auto-mocks from real traffic
CI/CD Testing	Slow, flaky test suites delaying releases	Fast, deterministic scenario replays
Cross-Service Debugging	Tracing requests across services manually	Complete call chains with method parameters
Test Documentation	Manual checklists and test case writing	Self-documenting from real behavior
Root Cause Analysis	DevTools, Kibana, SQL—hours of investigation	Click traceId → see exact failing method instantly

The BitDive Impact

For Business

75%

Fewer Production Incidents

Catch regressions before deployment

Faster Release Cycles

Eliminate slow, flaky test suites

60%

Reduced QA Tech Debt

No test framework maintenance

For QA & Development

80%

Less Time on Test Maintenance

Focus on analysis, not infrastructure

90%

Faster Root Cause Identification

See exact failing method instantly, not log archaeology

100%

Behavioral Coverage

Tests based on real traffic, not assumptions

Integrate with Your Development & AI Stack

Java

Kotlin

Spring

PostgreSQL

MySQL

MariaDB

MongoDB

Redis

Model Context Protocol

Ollama

Cursor

Windsurf

OpenAI

GitHub Copilot

Anthropic

Claude

Gemini

Grok

Mistral AI

DeepSeek

Perplexity

Cody

VS Code

Visual Studio

IntelliJ IDEA

Docker

Kubernetes

Azure

GitHub

GitLab

Keycloak

Ready to Transform Your QA Process?

Stop fighting test infrastructure. Start validating real behavior. Take your existing test scenarios and see them become stable, reproducible regression suites in 2 weeks.

Book A Demo