Forget about Unit Test, you get covered automatically.

This is the example repo for Test Gru. Test Gru will help you add unit test automatically. Here is the video to show how Test Gru works.

Test gru is still in testing phase. If you have any question, please connect to us: connect@gru.ai,or join our discord.

Currently, Test Gru only supports Node.js/TypeStript. We are gradually adding support for other languages.

Creat Test Gru Account

Log in at Gru.ai. Test Gru currently only supports use with GitHub accounts. You need a GitHub account to log in to gru.ai.

Enter Test Gru

Click the top left corner to select Test Gru.

Install Github Application

Follow the steps to install Test Gru.

Then select a repo, perform the configuration.

Use Example For Quick Start

1. Fork this repo
2. Install App and get configuration from code repo
3. dispatch src/user.ts
4. Test Gru submits a PR

grutest.yaml

Configuration file example

version: "0.1"
global:
  setup:
    - npm install
pipeline:
  runTest:
    // If your project uses ESLint and Prettier, you can configure the pre-stage here.
    // pre:
    //    - npx eslint --fix {{sourceFilePath}} 
    //    - npx prettier {{sourceFilePath}} --write
    exec:
       - npx vitest run {{testFilePath}}
    // If your project has certain requirements for the final submitted code, you can configure the post-stage here.
    // post:
    //   - npm run lint
    //   - npx tsc --noEmit
settings:
  // IF you allow TestGru to add export to your source code classes or functions when it needs to test your source code
  exportFunctionOrClass: allow
  // Location of the source code project
  include:
      - src
  // Location of the test files
  testPlacementStrategies:
    - type: co-located
      testFilePattern: "{{sourceFileName}}.spec.ts"

explanation

Name	Type	Required	Example Value or Default Value	Description
version	string	No	0.1	Version infomation
global	object	Required	-	Global confignation
global.setup	array	Required	-	Configuration Actions
global.cleanup	array	No	-	Cleanup Actions
pipeline	PipelineConfig	Required	-	Pipeline Configuration
pipeline.runTest	object	Required	-	Run Test Configuration
pipeline.runTest.exec	array	Required	-	Execute Command
pipeline.runTest.pre	array	No	-	Preprocessing Command
pipeline.runTest.post	array	No	-	Post-processing Command
pipeline.updateSource	object	No	-	Update Source Configuration
pipeline.updateSource.post	array	Required	-	Update Preprocessing Command
settings	SettingsConfig	No	-	Set Configuration
settings.exportFunctionOrClass	string (allow not-allow)	"allow"	-	Set Configuration
settings.include	array	No	src	Inclusions
settings.mockIgnore	array	No	["lodash", "ajv"]	Mock Exclusions
settings.testPlacementStrategies	array	No	The next chapter mainly introduces	Test Placement Strategy

About testPlacementStrategies

There are two main ways to organize test code.

centralized

If your project structure is as follows:

{% highlight TXT %} . ├── package.json ├── src │ └── sum.ts └── test └── sum.test.ts {% endhighlight %}

then you can configure testPlacementStrategies like this {% highlight YAML %} testPlacementStrategies:

• type: centralized testDir: test testFilePattern: "{{sourceFileName}}.test.ts" {% endhighlight %}

co-located

If your project structure is as follows: {% highlight TXT %} . ├── package.json └── src ├── sum.test.ts └── sum.ts {% endhighlight %}

then you can configure testPlacementStrategies like this

{% highlight YAML %} testPlacementStrategies: - type: co-located testFilePattern: "{{sourceFileName}}.test.ts" {% endhighlight %}

Trigger Test Gru to work

Auto Rrigger by Pull Request

When you complete the configuration, Test Gru will automatically take over your repository. Whenever you submit a PR, Test Gru will automatically detect software that requires unit tests and add tests for it.

After Gru completes writing the test code, it will run the tests. Once it confirms there are no issues with the test code, it will submit a PR with the unit test code to the current PR.

Manual Trigger

You can manually trigger Test Gru on the Gru.ai.

It can be triggered by PR or existing code files.

Python Example Project

This is a sample Python project that demonstrates various programming concepts and implementations. It's designed to be a good candidate for testing AI-powered unit test generation.

Project Structure

The project contains the following modules:

• calculator.py: A calculator class with basic and advanced mathematical operations
• string_utils.py: String processing utilities with various text manipulation functions
• data_structures.py: Custom data structure implementations (Priority Queue)
• main.py: Demo program showing usage of all modules

Features

Calculator

• Basic arithmetic operations
• Series calculations
• Compound interest calculation
• Operation history tracking

String Processor

• Word reversal
• Word frequency counting
• Palindrome detection
• Text truncation

Custom Priority Queue

• Priority-based item management
• Configurable maximum size
• Standard queue operations (push, pop, peek)
• Queue status checking

Running the Project

1. No external dependencies are required - the project uses only Python standard library

2. Run the main demo:

   python src/main.py

Testing Opportunities

This project provides various testing scenarios:

• Edge cases (empty inputs, invalid inputs)
• Mathematical precision
• Data structure operations
• String manipulations
• State management
• Error handling