AI app for it and development · no coding needed
Source-linked unit test generation console
Increase test coverage and catch bugs earlier while keeping engineers in control of what is merged.
Made for: Engineering teams maintaining codebases that need reliable unit tests

What it does for you
The problem
Manual unit test writing lags behind code changes, leaving coverage gaps and late bug discovery.
What it gives you
Reviewer-approved test files linked to source lines
What you give it
Repository codepull request diffsframework configurationcoverage targets
Build your own version of EarlyAI, DeepUnit and more
One app with what these 3 AI tools do, yours to keep and change: EarlyAI, DeepUnit, CodeBeaver.
Everything these tools do, in one app
- Automated unit test generation Automatically creates unit tests for your code, saving manual effort.Found in EarlyAI, DeepUnit, CodeBeaver
- High test coverage Generates tests that achieve high coverage to improve software reliability.Found in EarlyAI, DeepUnit
- Test navigation interfaces Provides easy navigation of generated tests through multiple interfaces.Found in EarlyAI
- Documentation suggestions Suggests method documentation to improve code readability and maintainability.Found in EarlyAI
- Pull request testing Quickly generates tests for recent code changes in pull requests.Found in EarlyAI, CodeBeaver
- Real-time status updates Provides real-time status updates and actionable code improvement insights.Found in EarlyAI
- IDE integration Integrates seamlessly with common developer environments like VSCode.Found in EarlyAI, DeepUnit
- Multi-language support Supports multiple programming languages and testing frameworks.Found in DeepUnit
- CI/CD integration Integrates with CI/CD pipelines and version control systems.Found in DeepUnit, CodeBeaver
- Coverage reports Provides detailed test coverage reports and suggestions for improving test suites.Found in DeepUnit
- Customizable test settings Allows customization of test generation settings to fit various project requirements.Found in DeepUnit
- User-friendly interface Offers a user-friendly interface with clear reporting features.Found in DeepUnit
- Edge case identification Updates existing test files to cover additional edge cases.Found in CodeBeaver
- Proactive bug spotting Leaves insightful pull request comments explaining test failures and suggesting fixes.Found in CodeBeaver
- Test maintenance Automatically maintains and updates tests when new code alters existing functionality.Found in CodeBeaver
How it works, step by step
- Detect untested functions and branches in selected code
- Generate unit tests for chosen functions and classes
- Target high coverage against configured thresholds
- Generate tests for recent pull request changes
- Update existing test files to cover additional edge cases
- Maintain and update tests when new code alters existing behavior
- Compare generated tests against source lines and locked behavior
- Compare the reviewed result with the recorded baseline and value assumptions
- Suggest method documentation for readability
- Provide real-time status updates and actionable improvement insights
- Produce detailed coverage reports with suite improvement suggestions
- Comment on pull requests explaining test failures and suggesting fixes
- Support multiple programming languages and testing frameworks
- Integrate with common IDEs and CI/CD pipelines
- Capture corrections and named-owner approval before merge
- Export a versioned reviewer-approved test file set with source references and unresolved questions
Build it yourself with your AI system
Build this app yourself, no coding needed
Start with a quick version you can try in a few minutes. Like it? Then build the full app by copying and pasting our step-by-step instructions: everything is prepared for you.
Sign in to see how to build it yourself
Build a quick version to try, or get the full app pack for Source-linked unit test generation console with the step-by-step building instructions. You don't need any technical skills: you copy, paste and answer a few questions. Both are included in the membership.
4 Have it built for you days to a few weeks
Rather not do it yourself, or want it fully tailored to your data, your way of working and your brand? Nexibeo builds Source-linked unit test generation console with you.
What's in the app pack
Included in the Complete AI Training membership.
- The building instructions your AI follows, step by step
- The questions your AI will ask you about your business before it starts
- A clickable demo you can open in your browser, to see how it should work
- A detailed blueprint of the screens, the information it keeps and the checks it runs
Become a member to get the app packAlready a member? Sign in
The files, for the technically curious
- START-HERE.mdHow to build it with your own AI (read first)3 KB
- README.mdOverview and links3 KB
- questions.mdQuestions to answer before you build2 KB
- prompt-cloudflare.mdThe full build prompt, hosted on Cloudflare25 KB
- prompt-vps.mdThe same build on your own server (Docker)25 KB
- spec.jsonData model, API, AI pipeline, acceptance criteria12 KB
- demo/index.htmlThe working demo on sample data196 KB
Questions
Do I need to know how to code?
No. You copy and paste the prompts on this page into ChatGPT or Claude, and the AI does the building. When it asks you something, you answer in your own words.
What does it cost?
The quick version, the app pack and the step-by-step instructions are for members: you pay the membership price, not a price per app (see the plans). Building the full app uses your own ChatGPT or Claude subscription. Putting it online is often cheap or no cost at the start, and your AI tells you before anything costs money.
How long does it take?
The quick version: about two minutes. The real app: an afternoon for a first version you can use, longer if you want every feature.
Can I change it to fit my business?
Yes. Tell your AI what to change in plain words, like “add a column for the price” or “use our logo and colours”. Or have Nexibeo build and customise it for you.
More detailsHow the AI works, safeguards and what to build first
Increase test coverage and catch bugs earlier while keeping engineers in control of what is merged. For engineering teams maintaining codebases that need reliable unit tests, convert repository code, pull request diffs, framework configuration and coverage targets into reviewer-approved test files linked to source lines. The benefit is a testable hypothesis, measured through accepted tests per engineering hour and defects caught before merge; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, collect repository code, pull request diffs, framework configuration and coverage targets, then follow this sequence: 1. Detect untested functions and branches in selected code. 2. Generate unit tests for chosen functions and classes. 3. Target high coverage against configured thresholds. Resolve uncertain cases with qualified reviewers, approve reviewer-approved test files linked to source lines, and measure accepted tests per engineering hour and defects caught before merge against a documented baseline.
How the AI works
Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the three stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. One repository and one test framework per pilot; final merge and behavior checks remain engineering. A model suggestion is never a verified fact, professional decision or authorization to act.
Safeguards
Preserve code ownership, source attribution, license accuracy and usage permissions. Engineers approve substantive changes and merge scope. One repository and one test framework; final merge and behavior checks remain engineering. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.
What to build first
Pilot scope: One repository and one test framework; final merge and behavior checks remain engineering. Implement one approved input format, a bounded representative case set and the first two task modules: detect untested functions and branches in selected code; generate unit tests for chosen functions and classes. Support the third module with operator review: target high coverage against configured thresholds. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.
What it can connect to
Customer-owned repositories, authorized code samples and permitted documentation sources. Version control systems, CI/CD pipelines, IDE extensions and coverage reporting destinations. Start with file exchange and validate destination specifications before promising direct commits. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.
The screens in detail
Primary screens: Repository and target selection, Generated test review, Coverage and delivery. Use a project list with repository status, a central diff view pairing generated tests with source lines, and a right-hand panel for coverage, framework settings and reviewer comments. Let users compare generated and existing tests side by side. Display draft, changes requested and approved states. Provide a pull request preview link with comments anchored to the relevant test. Make the task-specific outcome reviewer-approved test files linked to source lines visible beside its evidence, review state and value baseline.





