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cover-agent
CodiumAI Cover-Agent: An AI-Powered Tool for Automated Test Generation and Code Coverage Enhancement! ๐ป๐ค๐งช๐
Stars: 4223
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CodiumAI Cover Agent is a tool designed to help increase code coverage by automatically generating qualified tests to enhance existing test suites. It utilizes Generative AI to streamline development workflows and is part of a suite of utilities aimed at automating the creation of unit tests for software projects. The system includes components like Test Runner, Coverage Parser, Prompt Builder, and AI Caller to simplify and expedite the testing process, ensuring high-quality software development. Cover Agent can be run via a terminal and is planned to be integrated into popular CI platforms. The tool outputs debug files locally, such as generated_prompt.md, run.log, and test_results.html, providing detailed information on generated tests and their status. It supports multiple LLMs and allows users to specify the model to use for test generation.
README:
The logic and prompts for adding new imports for the generated tests have been improved.
We also added a usage examples file, with more elaborate examples of how to use the Cover Agent.
Added support for comprehensive logging to Weights and Biases. Set the WANDB_API_KEY
environment variable to enable this feature.
Cover-Agent now supports nearly any LLM model in the world, using LiteLLM package.
Notice that GPT-4 outperforms almost any open-source model in the world when it comes to code tasks and following complicated instructions.
However, we updated the post-processing scripts to be more comprehensive, and were able to successfully run the baseline script with llama3-8B
and llama3-70B models
, for example.
This repository includes the first known implementation of TestGen-LLM, described in the paper Automated Unit Test Improvement using Large Language Models at Meta.
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.
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.
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:
- Test Runner: Executes the command or scripts to run the test suite and generate code coverage reports.
- Coverage Parser: Validates that code coverage increases as tests are added, ensuring that new tests contribute to the overall test effectiveness.
- Prompt Builder: Gathers necessary data from the codebase and constructs the prompt to be passed to the Large Language Model (LLM).
- AI Caller: Interacts with the LLM to generate tests based on the prompt provided.
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. - Code Coverage tool: A Cobertura XML code coverage report is required for the tool to function correctly.
- For example, in Python one could use
pytest-cov
. Add the--cov-report=xml
option when running Pytest. - Note: We are actively working on adding more coverage types but please feel free to open a PR and contribute to
cover_agent/CoverageProcessor.py
- For example, in Python one could use
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/.
The Cover Agent can be installed as a Python Pip package or run as a standalone executable.
To install the Python Pip package directly via GitHub run the following command:
pip install git+https://github.com/Codium-ai/cover-agent.git
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.
Run the following command to install all the dependencies and run the project from source:
poetry install
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:
cover-agent \
--source-file-path "<path_to_source_file>" \
--test-file-path "<path_to_test_file>" \
--code-coverage-report-path "<path_to_coverage_report>" \
--test-command "<test_command_to_run>" \
--test-command-dir "<directory_to_run_test_command>" \
--coverage-type "<type_of_coverage_report>" \
--desired-coverage <desired_coverage_between_0_and_100> \
--max-iterations <max_number_of_llm_iterations> \
--included-files "<optional_list_of_files_to_include>"
You can use the example code below to try out the Cover Agent. (Note that the usage_examples file provides more elaborate examples of how to use the Cover Agent)
Follow the steps in the README.md file located in the templated_tests/python_fastapi/
directory to setup an environment, then return to the root of the repository, and run the following command to add tests to the python fastapi example:
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:
go install github.com/axw/gocov/[email protected]
go install github.com/AlekSi/[email protected]
and then run the following command:
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 $(pwd) \
--coverage-type "cobertura" \
--desired-coverage 70 \
--max-iterations 1
For an example using java cd
into templated_tests/java_gradle
, set up the project following the README.md.
To work with jacoco coverage reporting, follow the README.md Requirements section:
and then run the following command:
cover-agent \
--source-file-path="src/main/java/com/davidparry/cover/SimpleMathOperations.java" \
--test-file-path="src/test/groovy/com/davidparry/cover/SimpleMathOperationsSpec.groovy" \
--code-coverage-report-path="build/reports/jacoco/test/jacocoTestReport.csv" \
--test-command="./gradlew clean test jacocoTestReport" \
--test-command-dir=$(pwd) \
--coverage-type="jacoco" \
--desired-coverage=70 \
--max-iterations=1
A few debug files will be outputted locally within the repository (that are part of the .gitignore
)
-
run.log
: A copy of the logger that gets dumped to yourstdout
-
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
If you set an environment variable WANDB_API_KEY
, the prompts, responses, and additional information will be logged to Weights and Biases.
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:
- Export any environment variables needed by the supported LLM following the LiteLLM instructions.
- Call the name of the model using the
--model
option when calling Cover Agent.
For example (as found in the LiteLLM Quick Start guide):
export VERTEX_PROJECT="hardy-project"
export VERTEX_LOCATION="us-west"
cover-agent \
...
--model "vertex_ai/gemini-pro"
export OPENAI_API_KEY="<your api key>" # If <your-api-base> requires an API KEY, set this value.
cover-agent \
...
--model "openai/<your model name>" \
--api-base "<your-api-base>"
This section discusses the development of this project.
Before merging to main make sure to manually increment the version number in cover_agent/version.txt
at the root of the repository.
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
.
You can build the binary locally simply by invoking the make installer
command. This will run PyInstaller locally on your machine. Ensure that you have set up the poetry project first (i.e. running poetry install
).
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
- [x] 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'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.
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BricksLLM
BricksLLM is a cloud native AI gateway written in Go. Currently, it provides native support for OpenAI, Anthropic, Azure OpenAI and vLLM. BricksLLM aims to provide enterprise level infrastructure that can power any LLM production use cases. Here are some use cases for BricksLLM: * Set LLM usage limits for users on different pricing tiers * Track LLM usage on a per user and per organization basis * Block or redact requests containing PIIs * Improve LLM reliability with failovers, retries and caching * Distribute API keys with rate limits and cost limits for internal development/production use cases * Distribute API keys with rate limits and cost limits for students
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uAgents
uAgents is a Python library developed by Fetch.ai that allows for the creation of autonomous AI agents. These agents can perform various tasks on a schedule or take action on various events. uAgents are easy to create and manage, and they are connected to a fast-growing network of other uAgents. They are also secure, with cryptographically secured messages and wallets.
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griptape
Griptape is a modular Python framework for building AI-powered applications that securely connect to your enterprise data and APIs. It offers developers the ability to maintain control and flexibility at every step. Griptape's core components include Structures (Agents, Pipelines, and Workflows), Tasks, Tools, Memory (Conversation Memory, Task Memory, and Meta Memory), Drivers (Prompt and Embedding Drivers, Vector Store Drivers, Image Generation Drivers, Image Query Drivers, SQL Drivers, Web Scraper Drivers, and Conversation Memory Drivers), Engines (Query Engines, Extraction Engines, Summary Engines, Image Generation Engines, and Image Query Engines), and additional components (Rulesets, Loaders, Artifacts, Chunkers, and Tokenizers). Griptape enables developers to create AI-powered applications with ease and efficiency.