Skip to main content
Join the official 2019 Python Developers SurveyStart the survey!

Model-based testing in Python

Project description

PyModel is an open-source model-based testing framework in Python.

In model-based testing, you code a model that can generate as many test cases as needed. The model also checks the test outcomes. Model-based testing is helpful where so many test cases are needed that it is not feasible to code them all by hand.

In the samples included with PyModel, there are models and test scripts for network sockets, a communication protocol, embedded controllers, some data structures, a multithreaded application, and a web application.

PyModel includes an analyzer for validating models, visualizing their behavior, and checking their safety properties.

PyModel can generate offline tests which are similar to unit tests, but the typical way to use PyModel is on-the-fly testing, where the test runner uses the model to compute the test run as it executes, so test runs can be as long as needed. On-the-fly testing can cope with nondeterminism and asynchrony in the system under test.

PyModel can combine models using composition, guide tests through programmed scenarios, and focus test coverage according to programmed strategies.

PyModel provides three main programs:

  • pma, PyModel analyzer: generates a finite state machine (FSM)
    and computes properties by exploring a model program, FSM, test suite, or a product of these.
  • pmg, PyModel graphics: generates a file of graphic commands from an FSM,
    that can be processed by the Graphviz dot command to produce graphics files in various formats including svg, pdf, and ps.
  • pmt, PyModel tester: displays traces, generates tests offline,
    executes offline tests, or generates and executes tests on-the-fly.

There is also a fourth program:

  • pmv, PyModel viewer: invokes pma, pmg, and the Graphiz dot
    command (to display the graphics generated by pmg). The pmv program provides brevity and convenience, so analysis and display can be accomplished by a single command.

Use pma and pmg (or pmv) to visualize and preview the behavior of pmt. Every path through the graph created by pma (and drawn by pmg) is a trace (test run) that may be generated by pmt, when pma and pmt are invoked with the same arguments. The pma program is also useful on its own for visualization and safety analysis.

PyModel work in progress is available at GitHub:

The PyModel distribution is also available at the author’s web page:

PyModel requires Python 2.6 or higher (because it uses itertools.product)

PyModel is influenced by NModel, but is not a translation or re-implementation.

PyModel is covered by the BSD License.

Code and documents are copyright (C) 2009-2013, Jonathan Jacky.

Revised May 2013

Project details

Download files

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

Files for PyModel, version 1.0
Filename, size File type Python version Upload date Hashes
Filename, size PyModel-1.0.tar.gz (673.9 kB) File type Source Python version None Upload date Hashes View hashes
Filename, size (813.2 kB) File type Source Python version None Upload date Hashes View hashes

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN SignalFx SignalFx Supporter DigiCert DigiCert EV certificate StatusPage StatusPage Status page