Jest
Mocha
PyTest
JUnit
NUnit
RSpec
PHPUnit
Cypress
xUnit
Playwright
Selenium
Join our community to see how developers are using Workik AI everyday.
Supported AI models on Workik
GPT 5 Mini, GPT 5, GPT 4.1 Mini, GPT o4 Mini, GPT o3
Gemini 2.5 Flash Preview, Gemini 2.0 Flash, Gemini 1.5 Pro
Claude 4 Sonnet, Claude 3.5 Haiku, Claude 3.7 Sonnet
Deepseek Reasoner, Deepseek Chat, Deepseek R1(High)
Llama 4 Maverick 17B Instruct, Llama 3.3 70B, Llama 3.1 405B Instruct
Pixtral Large, Mistral 8x7B Instruct, Mistral Small, Mistral Large, Codestral
Models availability might vary based on your plan on Workik
Features
Spot Untested Logics
AI analyzes branches, conditions, and edge cases your current tests might miss & auto-generates them.
Detect Redundant Tests
Identify duplicate or ineffective tests that don’t improve coverage or assert meaningful outcomes
Integrate With CI Pipelines
Run coverage checks on every github push, and auto-report deltas via GitHub or Slack.
Enforce Coverage Thresholds
Set minimum coverage percentage per module & block merges in CI if critical areas fall below target.
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TESTIMONIALS
Real Stories, Real Results with Workik
I used Workik AI to auto-generate missing tests across our microservices—pushed coverage from 62% to 90%.
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Daniel Cruz
DevOps Lead
I plugged AI into our GitHub workflow and it instantly blocked low-coverage merges. It’s an absolute game changer!
Jared Lee
Engineering Manager
Workik AI flagged untested edge cases in our service layer that even our senior QA team missed.
Priya Deshmukh
Backend Engineer
What are the popular use cases of Workik AI-Powered Test Coverage Analyzer for developers?
Workik AI helps developers analyze and optimize test coverage in their development workflow. Some of its popular use cases include but are not limited to:
* Backfilling unit tests in legacy Java or Python services with no prior test coverage
* Scanning PR diffs in CI to detect untested conditions before merge
* Generating integration tests for Express, Django, or Go API endpoints
* Identifying skipped edge cases in async handlers, error blocks, or business rules
* Improving test depth in microservices while ignoring unrelated modules
* Mapping existing tests to code to find redundant or ineffective coverage
* Creating test templates and mocks during test-driven development (TDD)
* Highlighting untested React/Vue component props and suggesting frontend unit tests
Is adding context necessary for Workik AI’s Test Coverage Analyzer?
No, it’s completely optional. You can add the following types of context to get more precise test analysis and suggestions:
* Framework context – e.g., specify Jest for frontend, Pytest for backend, Go Test for microservices
* Directory/module scope – focus on auth/, api/, or utils/ folders for targeted analysis
* Tech stack hints – let Workik know if you’re using React, Flask, Spring Boot, etc., to format tests correctly
* Schema or model definitions – e.g., define a User model so generated tests match your data structures
* Existing test coverage reports – help AI focus on gaps rather than what’s already tested
* CI/CD rules or merge criteria – align test suggestions with your team’s quality gates
Can I integrate AI-Powered Test Coverage Analyzer with my existing CI/CD pipeline?
Yes. Workik AI plugs directly into your GitHub, GitLab, or Bitbucket CI workflows. You can set it to run on every pull request and even enforce minimum coverage thresholds by module (e.g., services must have 85%+). If a PR introduces untested logic, Workik AI can flag it or block the merge entirely.
What kinds of tests can Workik AI generate?
Workik AI supports unit, integration, and even edge case test generation for major frameworks like Jest, Pytest, JUnit, Go Test, and more. For instance, in a Python Flask app, it can generate Pytest functions for uncovered API routes using actual input-output pairs based on your route definitions and models. It also generates mocks and fixtures to speed up test writing.
How does Workik handle large monorepos or microservice projects?
Workik AI is context-aware. It lets you target coverage by module, so you can run scans on specific services or shared utilities. It’s especially useful for microservices where developers can quickly see which endpoints or handlers lack test coverage without touching unrelated code. In monorepos, Workik AI maps test coverage to subdirectories, Git repos, or even ticket references (e.g., Jira IDs in commit messages).
Can Workik AI help with legacy projects that have zero tests?
Yes. Workik AI is great for test backfilling in legacy codebases. You can provide it at older monolithic apps—say a Java Spring service or a Node.js Express API, and it will highlight untested routes, functions, and edge cases, then auto-generate test stubs using tools like JUnit or Mocha.
Can Workik AI help with TDD or writing tests first?
Yes. Developers using Test-Driven Development can leverage Workik AI to generate test templates for new functions before implementation. For example, describe your function in a prompt or write a stub, and Workik will scaffold a test file with assertions, mocks, and fixtures—ready to guide your code.
Does Worki AI’s Test Coverage Analyzer support frontend testing as well?
Absolutely. For React, Vue, or Angular apps, Workik AI can analyze component-level test coverage and generate missing tests using Jest, Vitest, Cypress, or Playwright. It identifies untested props, event handlers, and render conditions & then generates frontend specific test cases.
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