qodo cover
Basic Information
Qodo Cover is an open source developer tool that uses generative AI to automatically create and extend unit tests to increase code coverage. It runs as a CLI tool, a pip-installable Python package, or a standalone binary and is intended for use locally or integrated into CI workflows. The project orchestrates test execution, parses coverage reports, builds prompts from code and test context, and calls Large Language Models to synthesize tests. It includes examples for Python, Go, and Java and requires a code coverage report (Cobertura or jacoco formats) and an LLM API key. The README notes a record-and-replay mode for LLM outputs and mentions ongoing roadmap items such as broader coverage types, repo-wide scanning, and CI connectors.
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App Details
Features
The repository provides a Test Runner to execute test commands and collect coverage, a Coverage Parser that validates coverage changes, a Prompt Builder that gathers code context for LLM prompts, and an AI Caller to request generated tests from models. CLI flags allow specifying source and test file paths, project root, coverage report path, test command, desired coverage and iteration limits. It supports recording LLM responses to stored_responses for replay, integrates with LiteLLM to reach many LLM providers and supports OpenAI and Azure-compatible endpoints. Additional outputs include run.log and test_results.html, optional Weights and Biases logging, and templated_examples for multiple languages.
Use Cases
Qodo Cover automates repetitive and time-consuming test-generation tasks so development teams can raise test coverage more efficiently. By validating that newly generated tests actually increase coverage it reduces wasted effort. The record-and-replay feature saves API credits and enables deterministic re-runs. Language examples and coverage-format support make it applicable across Python, Go, and Java projects. CLI and binary modes allow integration into developer workflows and CI pipelines, and the tool is designed to be extended with additional coverage types, CI connectors, and repo-wide scanning to target changed code. The project also serves as an open research platform for experimenting with AI test-generation techniques.
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