## Table of Contents
- [News and Updates](#news-and-updates)
- [Overview](#overview)
- [Installation and Usage](#installation-and-usage)
- [Development](#development)
- [Roadmap](#roadmap)
## News and Updates
### 2024-05-09:
#### This repository includes the first known implementation of TestGen-LLM, described in the paper [Automated Unit Test Improvement using Large Language Models at Meta](https://arxiv.org/abs/2402.09171).
# Cover-Agent
Welcome to Cover-Agent. This focused project utilizes Generative AI to automate and enhance the generation of tests (currently mostly unit tests), aiming to streamline development workflows. Cover-Agent can run via a terminal, and is planned to be integrated into popular CI platforms.
[](https://youtu.be/fIYkSEJ4eqE?feature=shared)
We invite the community to collaborate and help extend the capabilities of Cover Agent, continuing its development as a cutting-edge solution in the automated unit test generation domain. We also wish to inspire researchers to leverage this open-source tool to explore new test-generation techniques.
## Overview
This tool is part of a broader suite of utilities designed to automate the creation of unit tests for software projects. Utilizing advanced Generative AI models, it aims to simplify and expedite the testing process, ensuring high-quality software development. The system comprises several components:
1. **Test Runner:** Executes the command or scripts to run the test suite and generate code coverage reports.
2. **Coverage Parser:** Validates that code coverage increases as tests are added, ensuring that new tests contribute to the overall test effectiveness.
3. **Prompt Builder:** Gathers necessary data from the codebase and constructs the prompt to be passed to the Large Language Model (LLM).
4. **AI Caller:** Interacts with the LLM to generate tests based on the prompt provided.
## Installation and Usage
### Requirements
Before you begin, make sure you have the following:
- `OPENAI_API_KEY` set in your environment variables, which is required for calling the OpenAI API.
If running directly from the repository you will also need:
- Python installed on your system.
- Poetry installed for managing Python package dependencies. Installation instructions for Poetry can be found at [https://python-poetry.org/docs/](https://python-poetry.org/docs/).
### Standalone Runtime
The Cover Agent can be installed as a Python Pip package or run as a standalone executable.
#### Python Pip
To install the Python Pip package directly via GitHub run the following command:
```
pip install git+https://github.com/Codium-ai/cover-agent.git
```
#### Binary
The binary can be run without any Python environment installed on your system (e.g. within a Docker container that does not contain Python). You can download the release for your system by navigating to the project's [release page](https://github.com/Codium-ai/cover-agent/releases).
### Repository Setup
Run the following command to install all the dependencies and run the project from source:
```shell
poetry install
```
### Running the Code
After downloading the executable or installing the Pip package you can run the Cover Agent to generate and validate unit tests. Execute it from the command line by using the following command:
```shell
cover-agent \
--source-file-path "" \
--test-file-path "" \
--code-coverage-report-path "" \
--test-command "" \
--test-command-dir "" \
--coverage-type "" \
--desired-coverage \
--max-iterations \
--included-files ""
```
You can use the example projects within this repository to run this code as a test.
Follow the steps in the README.md file located in the `templated_tests/python_fastapi/` directory, then return to the root of the repository and run the following command to add tests to the **python fastapi** example:
```shell
cover-agent \
--source-file-path "templated_tests/python_fastapi/app.py" \
--test-file-path "templated_tests/python_fastapi/test_app.py" \
--code-coverage-report-path "templated_tests/python_fastapi/coverage.xml" \
--test-command "pytest --cov=. --cov-report=xml --cov-report=term" \
--test-command-dir "templated_tests/python_fastapi" \
--coverage-type "cobertura" \
--desired-coverage 70 \
--max-iterations 10
```
For an example using **go** `cd` into `templated_tests/go_webservice`, set up the project following the `README.md`.
To work with coverage reporting, you need to install `gocov` and `gocov-xml`. Run the following commands to install these tools:
```shell
go install github.com/axw/gocov/gocov@v1.1.0
go install github.com/AlekSi/gocov-xml@v1.1.0
```
and then run the following command:
```shell
cover-agent \
--source-file-path "app.go" \
--test-file-path "app_test.go" \
--code-coverage-report-path "coverage.xml" \
--test-command "go test -coverprofile=coverage.out && gocov convert coverage.out | gocov-xml > coverage.xml" \
--test-command-dir $(pwd) \
--coverage-type "cobertura" \
--desired-coverage 70 \
--max-iterations 1
```
Try and add more tests to this project by running this command at the root of this repository:
```shell
poetry run cover-agent \
--source-file-path "cover_agent/main.py" \
--test-file-path "tests/test_main.py" \
--code-coverage-report-path "coverage.xml" \
--test-command "poetry run pytest --junitxml=testLog.xml --cov=templated_tests --cov=cover_agent --cov-report=xml --cov-report=term --log-cli-level=INFO" \
--coverage-type "cobertura" \
--desired-coverage 70 \
--max-iterations 1 \
--model "gpt-4o"
```
Note: If you are using Poetry then use the `poetry run cover-agent` command instead of the `cover-agent` run command.
### Outputs
A few debug files will be outputted locally within the repository (that are part of the `.gitignore`)
* `generated_prompt.md`: The full prompt that is sent to the LLM
* `run.log`: A copy of the logger that gets dumped to your `stdout`
* `test_results.html`: A results table that contains the following for each generated test:
* Test status
* Failure reason (if applicable)
* Exit code,
* `stderr`
* `stdout`
* Generated test
### Using other LLMs
This project uses LiteLLM to communicate with OpenAI and other hosted LLMs (supporting 100+ LLMs to date). To use a different model other than the OpenAI default you'll need to:
1. Export any environment variables needed by the supported LLM [following the LiteLLM instructions](https://litellm.vercel.app/docs/proxy/quick_start#supported-llms).
2. Call the name of the model using the `--model` option when calling Cover Agent.
For example (as found in the [LiteLLM Quick Start guide](https://litellm.vercel.app/docs/proxy/quick_start#supported-llms)):
```shell
export VERTEX_PROJECT="hardy-project"
export VERTEX_LOCATION="us-west"
cover-agent \
...
--model "vertex_ai/gemini-pro"
```
## Development
This section discusses the development of this project.
### Versioning
Before merging to main make sure to manually increment the version number in `cover_agent/version.txt` at the root of the repository.
### Running Tests
Set up your development environment by running the `poetry install` command as you did above.
Note: for older versions of Poetry you may need to include the `--dev` option to install Dev dependencies.
After setting up your environment run the following command:
```
poetry run pytest --junitxml=testLog.xml --cov=templated_tests --cov=cover_agent --cov-report=xml --cov-report=term --log-cli-level=INFO
```
This will also generate all logs and output reports that are generated in `.github/workflows/ci_pipeline.yml`.
## Roadmap
Below is the roadmap of planned features, with the current implementation status:
- [x] Automatically generates unit tests for your software projects, utilizing advanced AI models to ensure comprehensive test coverage and quality assurance. (similar to Meta)
- [x] Being able to generate tests for different programming languages
- [ ] Being able to deal with a large variety of testing scenarios
- [ ] Generate a behavior analysis for the code under test, and generate tests accordingly
- [ ] Check test flakiness, e.g. by running 5 times as suggested by TestGen-LLM
- [ ] Cover more test generation pains
- [ ] Generate new tests that are focused on the PR changeset
- [ ] Run over an entire repo/code-base and attempt to enhance all existing test suites
- [ ] Improve usability
- [ ] Connectors for GitHub Actions, Jenkins, CircleCI, Travis CI, and more
- [ ] Integrate into databases, APIs, OpenTelemetry and other sources of data to extract relevant i/o for the test generation
- [ ] Add a setting file
## CodiumAI
CodiumAI's mission is to enable busy dev teams to increase and maintain their code integrity.
We offer various tools, including "Pro" versions of our open-source tools, which are meant to handle enterprise-level code complexity and are multi-repo codebase aware.
