Make AI Test Management Easy With CoTester
Generate test cases, run them in real environments, and capture results with full context. Know exactly what’s been tested and what hasn’t with our AI-powered test management tool. Keep your tests stable as your application changes.
Generate Test Cases With AI
Upload user stories or link your Jira directly. As an AI test management platform, CoTester turns acceptance criteria and user flows into structured test cases with defined steps, validations, and expected outcomes. You review each test, adjust coverage per feature, and approve before execution. It just takes minutes.
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Run Tests Across Real Environments
Trigger test runs through your CI pipeline or run them on demand without switching tools. Our AI-powered test management tool executes each step against real browsers and devices, so validation reflects actual user behavior. Failures surface at the exact step they occur.
Request Free TrialCapture Results and Defects
When a test fails, our AI test management tool records it at that step with screenshots, execution logs, and environment data. Execution and defect reporting stay tied to the same test run, so issues can be reproduced and resolved without additional investigation.
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Keep Tests Aligned with Changes
Existing tests continue to run as UI layouts change without requiring step-level rewrites. The system adapts during execution by adjusting element handling while preserving the original test flow.
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Control Test Data and State
Run tests with controlled data and a known environment state. Reset between executions, avoid data conflicts, and reproduce failures under the same conditions. Each run starts clean, so results remain consistent.
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Frequently Asked Questions (FAQs)
01
What is AI test management?
AI test management refers to using artificial intelligence to generate, execute, maintain, and track test cases within a single system. It is commonly implemented through AI-driven test management software that connects requirements, execution, and results.
01
What is AI test management?
AI test management refers to using artificial intelligence to generate, execute, maintain, and track test cases within a single system. It is commonly implemented through AI-driven test management software that connects requirements, execution, and results.
02
How does AI generate test cases from requirements?
AI in test case management uses user stories, acceptance criteria, and workflows to create structured test cases with defined steps, validations, and expected outcomes. These test cases can be reviewed and approved before execution.
02
How does AI generate test cases from requirements?
AI in test case management uses user stories, acceptance criteria, and workflows to create structured test cases with defined steps, validations, and expected outcomes. These test cases can be reviewed and approved before execution.
03
Can AI test management support both manual and automated testing?
Yes. Automated test management allows teams to run AI-generated test cases directly and reuse them across multiple test cycles without maintaining separate manual and automated workflows.
03
Can AI test management support both manual and automated testing?
Yes. Automated test management allows teams to run AI-generated test cases directly and reuse them across multiple test cycles without maintaining separate manual and automated workflows.
04
How are defects captured during AI-driven test execution?
Failures are identified at the step where they occur. In AI-powered test management systems, execution data such as logs, screenshots, and environment details are captured alongside the failure for easier debugging.
04
How are defects captured during AI-driven test execution?
Failures are identified at the step where they occur. In AI-powered test management systems, execution data such as logs, screenshots, and environment details are captured alongside the failure for easier debugging.
05
How does AI handle changes in UI or application workflows?
Generative AI test management systems adapt to UI changes during execution by updating how elements are identified and interacted with. For workflow or logic changes, tests can be reviewed and updated to reflect the new behavior.
05
How does AI handle changes in UI or application workflows?
Generative AI test management systems adapt to UI changes during execution by updating how elements are identified and interacted with. For workflow or logic changes, tests can be reviewed and updated to reflect the new behavior.
06
How is consistency maintained across test runs?
AI test data management ensures that tests run with controlled datasets and consistent environment states. This helps reproduce failures reliably and prevents inconsistencies caused by leftover data or configuration drift.
06
How is consistency maintained across test runs?
AI test data management ensures that tests run with controlled datasets and consistent environment states. This helps reproduce failures reliably and prevents inconsistencies caused by leftover data or configuration drift.