Qodo-Cover is an AI-assisted tool that automatically generates unit tests to improve code coverage. It can run in GitHub CI or locally as a CLI, with usage examples showing a cover-agent command and integration steps.
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What it is
Qodo Cover aims to help efficiently increase code coverage by automatically generating qualified tests to extend code coverage. Qodo Cover can run in your GitHub CI workflow or locally as a CLI tool.
How it works
The project contains a Test Runner, Coverage Parser, Prompt Builder, and AI Caller to interact with an LLM for test generation and coverage validation.
Getting started
Requirements include setting OPENAI_API_KEY and having a Cobertura XML code coverage report. Installation options include:
pip install git+https://github.com/qodo-ai/qodo-cover.git
poetry install
cover-agent \
--source-file-path "<path_to_source_file>" \
--test-file-path "<path_to_test_file>" \
--project-root "<path_to_project_root>" \
--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>"
poetry install
cover-agent \
--source-file-path "templated_tests/python_fastapi/app.py" \
--test-file-path "templated_tests/python_fastapi/test_app.py" \
--project-root "templated_tests/python_fastapi" \
--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
poetry run python tests_integration/run_test_all.py --record-mode
Documentation and usage details
The README describes multiple language examples (Python, Go, Java) under a cover-agent usage pattern and highlights a Record & Replay feature for saving LLM responses. It also notes that OpenAI/LiteLLM-based models are used and provides endpoints/examples for OpenAI, Vertex/Azure configurations. It includes commands to set up and run tests, with outputs such as run.log and test_results.html.
Recent releases
Latest releases include:
- 0.3.10 Release 0.3.10 (2025-05-21)
- 0.3.9 Release 0.3.9 (2025-05-15)
- 0.3.8 Release 0.3.8 (2025-05-12)
- 0.3.7 Release 0.3.7 (2025-02-26)
- 0.3.6 Release 0.3.6 (2025-02-26)
Traction
Stars: 5575, Forks: 545, Open issues: 37
Caveats
License: AGPL-3.0. Created: 2024-05-18. Last push: 2026-04-05. Active maintenance status announced as of 2025-06-15 with note that repository is no longer maintained.