Coverage-AI

Table of Contents

• News and Updates
• Overview
• Installation and Usage
• Development
• Roadmap

News and Updates

2024-06-05:

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 Coverage Ai.

2024-06-01:

Added support for comprehensive logging to Weights and Biases. Set the WANDB_API_KEY environment variable to enable this feature.

2024-05-26:

Coverage-Ai 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.

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.

Coverage-Ai

Welcome to Coverage-Ai. This focused project utilizes Generative AI to automate and enhance the generation of tests (currently mostly unit tests), aiming to streamline development workflows. Coverage-Ai 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 Coverage Ai, 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.
• 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 coverage_ai/CoverageProcessor.py

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/.

Standalone Runtime

The Coverage Ai 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/khulnasoft/coverage-ai.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.

Repository Setup

Run the following command to install all the dependencies and run the project from source:

poetry install

Running the Code

After downloading the executable or installing the Pip package you can run the Coverage Ai to generate and validate unit tests. Execute it from the command line by using the following command:

coverage-ai \
  --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 Coverage Ai. (Note that the usage_examples file provides more elaborate examples of how to use the Coverage Ai)

Python

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:

coverage-ai \
  --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

Go

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/gocov@v1.1.0
go install github.com/AlekSi/gocov-xml@v1.1.0

and then run the following command:

coverage-ai \
  --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

Java

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:

coverage-ai \
  --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

Outputs

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 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

Additional logging

If you set an environment variable WANDB_API_KEY, the prompts, responses, and additional information will be logged to Weights and Biases.

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.
2. Call the name of the model using the --model option when calling Coverage Ai.

For example (as found in the LiteLLM Quick Start guide):

export VERTEX_PROJECT="hardy-project"
export VERTEX_LOCATION="us-west"

coverage-ai \
  ...
  --model "vertex_ai/gemini-pro"

OpenAI Compatible Endpoint

export OPENAI_API_KEY="<your api key>" # If <your-api-base> requires an API KEY, set this value.

coverage-ai \
  ...
  --model "openai/<your model name>" \
  --api-base "<your-api-base>"

Development

This section discusses the development of this project.

Versioning

Before merging to main make sure to manually increment the version number in coverage_ai/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=coverage_ai --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.

Building the binary locally

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).

Roadmap

Below is the roadmap of planned features, with the current implementation status:

• Automatically generates unit tests for your software projects, utilizing advanced AI models to ensure comprehensive test coverage and quality assurance. (similar to Meta)
  • 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

KhulnaSoft

KhulnaSoft'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.