AI Test Generation That Starts from Code
Testing lags behind development. TESTR closes the gap by reading your code, generating unit tests for every function, and analysing failures with root cause and suggested fixes.
TESTR is an AI test generation platform built by FLYTEBIT. It connects to your Git repository, analyses your code on every push, and generates unit test cases with executable test code across 11+ languages. Tests go through a review portal, run in CI/CD, and failures get analysed automatically.
What is AI test generation?
AI test generation is the use of AI to read your source code and automatically generate unit test cases and executable test code. It analyses each function and method, identifies branches, edge cases, and error paths, and produces tests that cover them. It generates both human-readable test case descriptions and executable code in frameworks like pytest, Jest, and JUnit.
The problem it solves is straightforward. Modern teams ship fast, but testing still lags behind. Unit tests are incomplete or missing. Functional test cases are written late, if at all. Test suites are flaky, outdated, and poorly organized. Failures generate noise instead of insight.
Traditional tools help you write tests. TESTR starts at the smallest unit, individual functions and methods, and builds a complete testing layer around them. Developers write code. TESTR takes care of unit testing, execution, reporting, and continuous improvement.
How TESTR generates tests
TESTR sits alongside your repositories and CI/CD pipelines. When you push code, a webhook notifies TESTR of the changes. It analyses the changed files and dependent units using AST and semantic analysis, then generates unit test cases for each function.
Unit test cases from code
For each function, TESTR generates unit test cases covering happy path, boundary conditions, error scenarios, and security variations. Each case includes purpose, risk if not tested, preconditions, and expected results.
Executable test code
pytest for Python, Jest for JavaScript and TypeScript, JUnit for Java, NUnit for C#, PHPUnit for PHP, GoogleTest for C++, go test for Go, RSpec for Ruby, and Rust's built-in framework. Mocks and stubs for external dependencies are generated automatically.
Human-in-the-loop review
Every generated test goes through a review portal. Developers see the diff, linked code snippets, and risk insights. They approve, reject, or modify before tests activate in production pipelines.
AI failure analysis
When a test fails, TESTR identifies which assertion failed, maps it back to the code region, and produces a root cause explanation in natural language. It also provides a step-by-step debugging narrative and a suggested code fix.
Test suites and categorisation
TESTR groups test cases into suites: smoke for fast verification, regression for pre-release checks, security for auth and validation, performance for critical endpoints. Suites can be curated manually or auto-grouped by feature and criticality.
Coverage that grows over time
TESTR learns from execution history, failures, and production signals. It identifies untested paths and proposes new test cases to fill gaps. Coverage expands rather than decaying as the codebase evolves.
What poor test coverage costs you
Teams ship fast, but testing lags behind. Unit tests are incomplete or missing. Functional test cases are written late, if at all. Test suites are flaky, outdated, and poorly organized. Failures generate noise instead of insight.
The result is predictable. Bugs reach production. Incidents take longer to resolve. Onboarding new developers takes longer because the codebase lacks the safety net of good tests. Senior engineers spend time writing boilerplate tests instead of focusing on architecture.
TESTR eliminates the unit test backlog. Every function gets tested. Every failure gets analysed. Coverage grows as the codebase evolves. Developers spend their time building features, not writing boilerplate tests.
AI test generation vs. inline AI and manual testing
Most teams use one of three approaches today: manual test writing, inline AI suggestions, or nothing. Here is how they compare.
How TESTR integrates with your stack
TESTR connects to GitHub, GitLab, and Bitbucket via webhooks. When you push code, TESTR analyses the changes and generates unit test cases for affected functions. Approved tests run in your existing CI/CD pipeline.
Your team manages everything through the TESTR portal. Review pending test cases, inspect failures, track coverage trends, and see execution history across all repositories. The portal provides the governance layer that inline AI tools lack.
FAQ
What is AI test generation?
AI test generation is the use of AI to read your source code and automatically generate unit test cases and executable test code. It analyses each function and method, identifies branches, edge cases, and error paths, and produces tests that cover them. It generates both human-readable test case descriptions and executable code in frameworks like pytest, Jest, and JUnit.
How does AI test generation work?
You connect your Git repository and configure a webhook. When you push code, the tool analyses changed files and dependent units using AST and semantic analysis. It identifies functions, methods, branches, edge cases, and error paths. For each function, it generates unit test cases covering happy path, boundary conditions, error scenarios, and security variations. It also produces executable test code with mocks and stubs for external dependencies. Tests go through a review portal where developers approve or reject them before they run in CI/CD.
Does AI test generation replace QA engineers?
No. AI test generation handles the repetitive work: writing unit tests for every function, generating mocks and stubs, running tests in CI/CD, and analysing failures. QA engineers focus on higher-value work: test strategy, exploratory testing, integration testing, and quality governance. The AI generates the unit test layer so QA teams can spend their time on complex scenarios that require human judgment.
What programming languages does TESTR support?
TESTR supports Python (pytest), JavaScript and TypeScript (Jest), Java (JUnit), C# (NUnit/xUnit), PHP (PHPUnit), C and C++ (Unity/GoogleTest), Go (go test), Ruby (RSpec), and Rust (built-in test framework). The parser analyses source code across all 11 languages without requiring language-specific configuration.
How does TESTR handle test failures?
When a test fails, TESTR identifies which assertion failed, maps the failure back to the specific code region and line, and produces a root cause explanation in natural language. It also provides a step-by-step debugging narrative and a suggested code fix. Developers can review the suggestion, apply the fix, and re-run the affected tests.
How much does AI test generation cost?
TESTR is a product with its own pricing. For custom test automation or consulting, a feasibility study starts from $2K. Full engagements start from $8K onwards. The final cost depends on the scope and engagement model. Visit testr.flytebit.com for TESTR product pricing.
See TESTR in Action
TESTR reads your code and writes the tests. Try it on your repository or talk to us about how it fits your pipeline.