Source-linked unit test generation console

For

Engineering teams maintaining codebases that need reliable unit tests

Solves

Manual unit test writing lags behind code changes, leaving coverage gaps and late bug discovery.

Delivers

Reviewer-approved test files linked to source lines

Built in

about 5 weeks of creation time, MVP in 6 days

Investment

$12,500 for the MVP, $42,500 for the full product

Run it

Inside your business, or as part of your offer to clients

Increase test coverage and catch bugs earlier while keeping engineers in control of what is merged.

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.
4. Generate tests for recent pull request changes.
5. Update existing test files to cover additional edge cases.
6. Maintain and update tests when new code alters existing behavior.
7. Compare generated tests against source lines and locked behavior.
8. Compare the reviewed result with the recorded baseline and value assumptions.
9. Suggest method documentation for readability.
10. Provide real-time status updates and actionable improvement insights.
11. Produce detailed coverage reports with suite improvement suggestions.
12. Comment on pull requests explaining test failures and suggesting fixes.
13. Support multiple programming languages and testing frameworks.
14. Integrate with common IDEs and CI/CD pipelines.
15. Capture corrections and named-owner approval before merge.
16. Export a versioned reviewer-approved test file set with source references and unresolved questions.

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

What goes in, what comes out

The workflow

1. In
   Start with

   Repository code, pull request diffs, framework configuration and coverage targets

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect repository code

4. 3

   Pull request diffs

5. 4

   Framework configuration and coverage targets

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewer-approved test files linked to source lines

AI does the heavy lifting, people stay in charge

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.

What your team sees

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.

Accounts and administration

Project ownership, repository versions, reviewer comments, approval states, usage allowances, generation limits, download history and a rights record for supplied code. Add organization access boundaries, named reviewers, usage caps, data retention controls, export logs and explicit approval for external actions.

Integrations and data access

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.

We build with our own AI software development factory, so most implementations take days to a few weeks of creation time, not months. You see working software at every step, and exact timing depends on availability.

1. 1

   Scoping call

   Day 1

   Thirty minutes on your process, your data and how you want to run it: for your own team, or for your clients. You get a fixed scope and price for the MVP.

2. 2

   MVP

   6 days

   One buyer segment, one recurring use case; first modules: detect untested functions and branches in selected code; generate unit tests for chosen functions and classes. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   7 days

   Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

4. 4

   Full product

   2 weeks

   Self-serve onboarding, billing, monitoring and the wider integration set.

5. 5

   Run and improve

   Monthly

   We host, monitor and improve it for a fixed monthly fee, or hand it over to your team. How the retainer works.

Why we start with an MVP

An MVP, or minimum viable product, is the smallest version that your users can actually work with. It is not a cheap version of the full solution. It is a test, built to answer the questions that decide whether the rest is worth building.

1. Pick the riskiest assumption. Here: will engineering teams maintaining codebases that need reliable unit tests use it to solve "manual unit test writing lags behind code changes, leaving coverage gaps and late bug discovery"?
2. Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
3. Run a paid pilot. Agree quality and outcome thresholds before the pilot using this measure: Accepted tests per engineering hour and defects caught before merge.
4. Measure, then decide. Track accepted tests per engineering hour and defects caught before merge; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. 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.

After the MVP. Once paid pilots prove usefulness, automate repeatable reviewed steps and add one verified source integration. Expand supported inputs and case volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around reviewer-approved test files linked to source lines. Retain the explicit scope boundary: One repository and one test framework; final merge and behavior checks remain engineering.

What the build depends on. Code upload and preview, asynchronous generation jobs, editable version history, reviewer access and tested export formats. High-fidelity production requires specialist engineering QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One repository and one test framework; final merge and behavior checks remain engineering.

A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.

Running costs per month

A rough indication of monthly hosting and AI model costs once it is live, not tested. Real costs depend on usage, file sizes and the models chosen.

Your brand, or this one

Run it under your own brand, or start from this concept style.

Selling it to your own clients: the go-to-market playbook

Pricing to test

Test a USD 300-1,500 fixed pilot for one defined repository package. Offer a monthly generation allowance after repeat demand. Quote complex multi-repository or specialist framework work separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewer-approved test file set. Recurring fees must specify volume, review depth and integration support. For exchanges, test a disclosed coordination or successful-service fee rather than holding customer funds. Reprice only after measuring real delivery labor; platform-build cost is separate from a commercial pilot fee.

Message to test

Increase test coverage and catch bugs earlier while keeping engineers in control of what is merged. Demonstrate a concrete reviewer-approved test file set using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Engineering teams maintaining codebases that need reliable unit tests professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewer-approved test file set from a small authorized input set, with a transparent calculation of accepted tests per engineering hour and defects caught before merge and no promised savings.

The first 30 days

1. Week 1: interview five engineering teams maintaining codebases that need reliable unit tests and inspect a recent example of manual unit test writing lags behind code changes, leaving coverage gaps and late bug discovery.
2. Week 2: prepare a consented or synthetic demonstration of the three task modules.
3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
4. Week 4: measure accepted tests per engineering hour and defects caught before merge, reviewer effort and repeat-purchase interest. This is a demand-validation plan, not a thirty-day full-product delivery promise.

Paid pilot

Agree quality and outcome thresholds before the pilot using this measure: Accepted tests per engineering hour and defects caught before merge. Continue only if the buyer accepts the actual output, the intended job outcome improves without unacceptable errors, and measured delivery cost fits willingness to pay. Revise or stop if access is unavailable, qualified review cannot be provided, or apparent savings disappear after corrections and support. Use held-out cases when comparing model quality; use a properly reviewed comparison design before making causal claims. Record missing cases and negative results alongside successful outputs.

Success metrics

Accepted tests per engineering hour and defects caught before merge; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewer-approved test files linked to source lines. Retain permissioned settings and reviewed examples, report realized value honestly, and sell increased volume or adjacent approved workflows only after contribution margin and quality remain acceptable.

Why clients would pick it

A reusable library of approved test patterns, framework constraints and review examples, together with reliable delivery for a narrow engineering niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for engineering teams maintaining codebases that need reliable unit tests. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

EarlyAI, DeepUnit and CodeBeaver, plus manual test writing and generic code assistants. Compare this product with the buyer's present method on accepted tests per engineering hour and defects caught before merge. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Generation attempts, code processing, storage, reviewer hours, client revision rounds and licensed source assets. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of reviewer-approved test files linked to source lines. Track cost per accepted output, including correction work, unsuccessful cases and support.

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.