AI app for it and development · no coding needed
Source-linked test generation and maintenance console
Reduce manual test upkeep while keeping tests in the team's own repository.
Made for: Engineering teams maintaining application test suites alongside frequent code changes
What it does for you
The problem
Code changes outpace test coverage, and broken or outdated tests consume developer time instead of catching real defects.
What it gives you
Reviewed, committed test code and triage decisions linked to their sources
What you give it
Repository accessbranch diffsnatural language promptsCI resultsreviewer feedback
Build your own version of Octomind MCP, Checksum AI and more
One app with what these 4 AI tools do, yours to keep and change: Octomind MCP, Checksum AI, Expect, Pi Copilot.
Everything these tools do, in one app
- Automated test generation Automatically creates tests based on natural language prompts, code changes, or user feedback.Found in Octomind MCP, Checksum AI, Expect and 1 more
- Test execution Runs the generated tests to verify application behavior.Found in Octomind MCP, Checksum AI, Expect
- Failure analysis and triage Analyzes test failures to diagnose issues and determine whether they are real bugs or broken tests.Found in Octomind MCP, Checksum AI
- Auto-fixing of test failures Automatically fixes broken tests to maintain test suite health.Found in Octomind MCP, Checksum AI
- Continuous test maintenance Keeps tests up-to-date as the application changes, reducing manual upkeep.Found in Octomind MCP, Checksum AI
- CI/CD integration Integrates with CI/CD pipelines to automate testing within development workflows.Found in Octomind MCP
- Natural language input Allows users to create tests using natural language prompts.Found in Octomind MCP
- Pull request testing Generates and runs tests automatically on every pull request.Found in Checksum AI
- Bug routing Sends real bugs to issue tracking or communication tools like Jira, Linear, or Slack.Found in Checksum AI
- Standard Playwright code Tests are committed as standard Playwright code in the team's own repository, avoiding lock-in.Found in Checksum AI
- Edge case targeting Focuses on testing auth boundaries and edge flows, not just easy passing cases.Found in Checksum AI
- Diff-based testing Scans unstaged changes or branch diffs to identify what to test.Found in Expect
- AI-generated test plans Creates test plans that outline scenarios to run against the application.Found in Expect
- Live browser execution Runs tests in a live browser for realistic end-to-end validation.Found in Expect
- Agent-driven workflow Integrates into developer or QA processes for faster feedback.Found in Expect
- Evaluation metric generation Automatically generates evaluation metrics for AI models and applications.Found in Pi Copilot
- Fast scoring models Uses proprietary models to provide fast and consistent scoring across many quality dimensions.Found in Pi Copilot
- Calibration with human feedback Supports calibration using human feedback, labeled data, or preference pairs.Found in Pi Copilot
- Integration with data and AI tools Integrates with tools like Sheets, PromptFoo, and GRPO.Found in Pi Copilot
- Export evaluation logic Allows exporting evaluation logic as code.Found in Pi Copilot
- Reward modeling and agent control Models can be used for reward modeling in reinforcement learning and agent control flow.Found in Pi Copilot
How it works, step by step
- Generate tests from natural language prompts, code changes or user feedback
- Scan unstaged changes or branch diffs to identify what to test
- Create AI-generated test plans outlining scenarios to run
- Target auth boundaries and edge flows, not just easy passing cases
- Run generated tests to verify application behavior
- Execute tests in a live browser for end-to-end validation
- Analyze failures to diagnose real bugs versus broken tests
- Auto-fix broken tests to maintain suite health
- Keep tests up to date as the application changes
- Generate and run tests automatically on every pull request
- Route real bugs to issue tracking or communication tools
- Commit tests as standard Playwright code in the team's own repository
- Generate evaluation metrics for AI models and applications
- Score outputs across quality dimensions with fast scoring models
- Calibrate scoring with human feedback, labeled data or preference pairs
- Export evaluation logic as code and support reward modeling and agent control flow
- Compare the reviewed result with the recorded baseline and value assumptions
- Capture corrections and named-owner approval before consequential use
- Export a versioned reviewed test and triage record 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 test generation and maintenance 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 test generation and maintenance 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 links4 KB
- questions.mdQuestions to answer before you build3 KB
- prompt-cloudflare.mdThe full build prompt, hosted on Cloudflare28 KB
- prompt-vps.mdThe same build on your own server (Docker)28 KB
- spec.jsonData model, API, AI pipeline, acceptance criteria14 KB
- demo/index.htmlThe working demo on sample data195 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
Reduce manual test upkeep while keeping tests in the team's own repository. For engineering teams maintaining application test suites alongside frequent code changes, convert repository diffs, natural language prompts, CI results and reviewer feedback into reviewed, committed test code and triage decisions linked to their sources. The benefit is a testable hypothesis, measured through accepted tests per developer hour and test-suite health after changes; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, collect repository access, branch diffs, natural language prompts, CI results and reviewer feedback, then follow this sequence: 1. Generate tests from natural language prompts, code changes or user feedback. 2. Scan unstaged changes or branch diffs to identify what to test. 3. Create AI-generated test plans outlining scenarios to run. 4. Target auth boundaries and edge flows. 5. Run generated tests and execute in a live browser. 6. Analyze failures to diagnose real bugs versus broken tests. 7. Auto-fix broken tests and keep tests up to date. 8. Route real bugs to issue tracking or communication tools. 9. Commit tests as standard Playwright code in the team's own repository. 10. Generate evaluation metrics and score outputs. 11. Calibrate scoring with human feedback. 12. Export evaluation logic as code. Resolve uncertain cases with qualified reviewers, approve reviewed test and triage records, and measure accepted tests per developer hour and test-suite health after changes against a documented baseline.
How the AI works
Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. Final test approval, bug triage and merge decisions remain with the engineering team. A model suggestion is never a verified fact, professional decision or authorization to act.
Safeguards
Preserve source attribution, test accuracy and usage permissions. Engineering teams approve substantive changes and merge scope. One repository and one CI provider; final test approval, bug triage and merge decisions remain with the engineering team. 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 CI provider; final test approval, bug triage and merge decisions remain with the engineering team. Implement one approved input format, a bounded representative case set and the first two task modules: generate tests from natural language prompts, code changes or user feedback; scan unstaged changes or branch diffs to identify what to test. Support the remaining modules with operator review: create AI-generated test plans; target auth boundaries and edge flows; run generated tests; execute in a live browser; analyze failures; auto-fix broken tests; keep tests up to date; generate and run tests on every pull request; route real bugs; commit tests as standard Playwright code; generate evaluation metrics; score outputs; calibrate scoring; export evaluation logic; support reward modeling. 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
Team-owned repositories, CI/CD pipelines, issue tracking and communication tools such as Jira, Linear or Slack, and data and AI tools such as Sheets, PromptFoo and GRPO. Start with file exchange and validate destination specifications before promising direct publishing. 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 prompt intake, Editable test and triage preview, CI and delivery status. Use a thumbnail gallery for repositories and runs, a large central editing canvas for generated test code and failure analysis, and a right-hand panel for sources, constraints and comments. Let users compare generated tests against existing suites side by side. Display draft, changes requested and approved states. Provide a reviewer link with comments anchored to the relevant test or failure. Make the task-specific outcome reviewed, committed test code and triage decisions visible beside its evidence, review state and value baseline.
