Skip to main content

A framework for causal testing using causal directed acyclic graphs.

Project description

Causal Testing Framework: A Causal Inference-Driven Software Testing Framework

example workflow codecov Documentation Status

Causal testing is a causal inference-driven framework for functional black-box testing. This framework utilises graphical causal inference (CI) techniques for the specification and functional testing of software from a black-box perspective. In this framework, we use causal directed acyclic graphs (DAGs) to express the anticipated cause-effect relationships amongst the inputs and outputs of the system-under-test and the supporting mathematical framework to design statistical procedures capable of making causal inferences. Each causal test case focuses on the causal effect of an intervention made to the system-under test. That is, a prescribed change to the input configuration of the system-under-test that is expected to cause a change to some output(s).

Causal Testing Workflow

Installation

See the readthedocs site for installation instructions.

Documentation

Further information on causal inference, the code, usage and more can be found on the docs

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

causal_testing_framework-4.3.0.tar.gz (1.2 MB view hashes)

Uploaded Source

Built Distribution

causal_testing_framework-4.3.0-py3-none-any.whl (1.2 MB view hashes)

Uploaded Python 3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page