Open Source Python Software Testing Tools - Page 3

The csvdatamix project aims to randomize CSV input data files in order to conceal the original state of the data. Similar to data masking or data transformation. Also has mapping abilities to translate back to the original state of the data.

CUBRID QA project contains a test-automation tool and many kinds of test cases for CUBRID database.

The experimental source code for the paper, "A Novel Recommendation Approach Based on Chronological Cohesive Units in Content Consuming"

Cinemagoer is a Python package useful to retrieve and manage the data of the IMDb movie database about movies, people, characters and companies. Platform-independent, it can retrieve data from both the IMDb's web server and a local copy of the whole db.

Automation of Unit and System Tests. Tests can be implemented in any language and on many platforms. The flexible approach enables the inclusion of many types of tests, such as memory leak checks (using valgrind), coding rule checks, complexity checks, etc. Tests are run by a simple call to hwut in a base directory of a project. In particular for C, HWUT supports make file generation using 'sos' and 'sols' modes. Remote control-able function stubs may be generated using the 'stub' mode. Test cases can be generated using the 'gen' mode, and state machine walkers by the 'sm_walker' mode. Over the last decade HWUT has matured towards a full fledged unit test tool for a wide variety of applications.

This repository collects concise flash cards that cover the core ideas of a traditional computer science curriculum with a focus on interview readiness. The cards distill topics like time and space complexity, classic data structures, algorithmic paradigms, operating systems, networking, and databases into short, testable prompts. They are designed for spaced-repetition style study so you can cycle frequently through fundamentals until recall feels automatic. Many cards point at canonical definitions or contrasts (e.g., stack vs. queue, BFS vs. DFS) to strengthen conceptual boundaries. The material favors clarity and breadth over exhaustive proofs, making it ideal for quick refreshers during a study plan. It complements longer resources by giving you a lightweight way to keep key concepts top of mind.

Deepchecks is the leading tool for testing and for validating your machine learning models and data, and it enables doing so with minimal effort. Deepchecks accompany you through various validation and testing needs such as verifying your data’s integrity, inspecting its distributions, validating data splits, evaluating your model and comparing between different models. While you’re in the research phase, and want to validate your data, find potential methodological problems, and/or validate your model and evaluate it. To run a specific single check, all you need to do is import it and then to run it with the required (check-dependent) input parameters. More details about the existing checks and the parameters they can receive can be found in our API Reference. An ordered collection of checks, that can have conditions added to them. The Suite enables displaying a concluding report for all of the Checks that ran.

Console-based tool that will allow for multiple types of automation testing to run in parallel. Written in Python

ETICS stands for "eInfrastructure for Testing, Integration and Configuration of Software". It provides software professionals with an "out-of-the-box" build and test system, powered with a build and test product repository.

EfficiencyGuardian extracts callgrind efficiency measures from individual CppUnit test cases to detect efficiency regresion. It includes a data mining web tool to browse historic results and TestFarm integration for unattended execution on commit.

This is a Python module that formats error information and appends it to a logfile in a format conductive to diagnostics. It is an alternative in some instances to the Python built-in logging module.

XAMPLE creates a separate, encrypted and independent LAMP environment on a system that does or does not already run a web server and database. It is useful to provide a customized environment for software refusing to work with the installed LAMP stack.

FAST is an Analysis and Simulation Toolbox (FAST) for Fuel Cells (FC) FAST-FC is the doctorate work of David B. Harvey and was developed with support from the U.S. DOE, Ballard, and Queen's University. Derivative works of FAST-FC include FC-APOLLO which is a forked branch of this project intended to capture the code state at the exit of the funded DOE project. FAST-FC is the open and active community branch. FAST-FC is developed and maintained by the original creator and developer, David B. Harvey. Features of FAST-fc include transport of gases and liquids, multi-step reaction kinetics, transient and steady state operation for performance and durability, and scalable dimensionality from 1D - 3D domains. Future development work is on-going in FAST-fc and our strategic development schedule includes membrane degradation, improved liquid water transport models through the application of pore networks, and extension to stack-level simulation.

Framework for writing decorator for FIT [Framework for Integrated Test][http://fit.c2.com/] fixtures. This project also provides a set of standard fit fixtures which can be used to decorate existing FIT table without modifying them.

Feisty Meow codebase offers a ton of useful bash scripts and implements several C++ libraries and many C++ applications. This is the Sourceforge mirror; visit our main site at: https://feistymeow.org

At the moment it's a collection of moderately useful python utility classes focusing mainly on unit test framework setup and development.

A beginning of adaptation of Sulley Fuzzing Framework for file fuzzing and working on Unix.

FTF (for Flexible Testing Framework) is a modular framework for testing, both automatic and manual, and quality assurance. FTF initial development has been done in the EDOS project, to support Linux distributions testing.

A suite of tools and extensions to Python to permit automatic specification-based testing with inline, incremental formal specification.

The FoxNuke program is written in python and uses Firefox in order to complete the distributed denial of service attack feature. Multiple headers are used from the Firefox browser, along with a personal configuration option for the Opera browser. The FoxNuke Program is still underdevelopment as of 8/24/17, full release is set to come out sometime during 2017-2018. If you would like to participate in the TESTING of this program and would like to help report bugs, etc. then please email leyvarosnel@gmail.com for further inquiries. Please note that Beta distributions will be limited. This whole project is being done open-source and for free as a hobby, so if I abandon this project it's just because I don't feel like working on it anymore.

Fressia Project is an effort to develop an Open Source general framework for Testing Automation. It's intended for users (testers) that want a simple tool which can be used just out of the box. It's does not requires any complicated configuration stuff

Visual regression tool used to compare baseline and updated instances of a website in a deployment pipeline. FRED is an opensource visual regression tool used to compare two instances of a website. FRED is responsible for automatic visual regression testing, with the purpose of ensuring that functionality is not broken by comparing a current(baseline) and an updated version of a website. The visual analysis computes the Normalized Mean Squared error and the Structural Similarity Index on the screenshots of the baseline and updated sites, while the visual AI looks at layout and content changes independently by applying image segmentation Machine Learning techniques to recognize high-level text and image visual structures. This reduces the impact of dynamic content yielding false positives. FRED is designed to be scalable. It has an internal queue and can process websites in parallel depending on the amount of RAM and CPUs (or GPUs) available.

FuzzBench is a large-scale, open research platform developed by Google to evaluate and benchmark fuzzers — automated software testing tools that detect vulnerabilities through randomized input generation. It provides a standardized, reproducible environment for comparing the performance and effectiveness of different fuzzing algorithms on real-world software targets. FuzzBench integrates with the OSS-Fuzz infrastructure, allowing it to run experiments on authentic open source projects and collect meaningful data on crash discovery rates, code coverage, and bug-finding efficiency. The service includes an easy-to-use API for integrating custom fuzzers and an automated reporting system that generates detailed statistical analyses, comparative graphs, and significance testing. By running experiments at Google scale, FuzzBench ensures consistent, unbiased, and data-driven evaluations that support academic and industrial fuzzing research.

GNU/Linux Desktop Testing Project (GNU/LDTP) is created with the goal of producing high quality test automation frameworks and cutting-edge tools that can be used to test the Linux Desktop and improve it.

Gefira is an umbrella project for various opensource-related projects, usually backend or middleware oriented.