Software testing python tutorials and videos

Turning Postman Collections into Committable Pytest Suites

Software Testing Magazine — Tue, 23 Jun 2026 15:34:50 +0000

In this article, Mikhail Golikov, the sole QA on a seven-team backend platform, explains how he turned a drawer full of unrun Postman collections into committable pytest suites, walks through the conversion request by request, and shows the folder-scoped command-line tool he built so one team’s pattern could scale to seven. Auhor: Mikhail Golikov We had a dozen Postman collections and not one of them ran on its own. They were good collections. Someone had sat down and captured the endpoints, the headers, the example bodies, the auth flow. New engineers opened them to learn how a service behaved. When an API changed, somebody usually remembered to update the collection. Usually. The problem was that the collections were documentation pretending to be tests. A person had to open Postman, pick the right environment, and click Send, request by request, reading each response with their own eyes. Nothing failed a build. Nothing ran at night. If an endpoint started returning a 500 on Tuesday, we found out when a human got around to clicking it, which on a seven-team backend platform with one QA is not a schedule you want to depend on. I was that one QA. The platform was backend e-commerce, microservices, seven teams shipping into shared services. My job was to keep regression honest across all of it, and a stack of collections that only ran when I clicked them was not regression. It was a to-do list. Why teams keep Postman collections but never run them in [...]

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How AI Python Coding Improves Software Quality

Software Testing Magazine — Tue, 30 Apr 2024 13:54:50 +0000

Python coding has never been easier. You can now create high-quality code faster with AI technology. Coders no longer have to spend hours brainstorming ideas. You can create better code with more effective solutions for your coding problems without your friends’ or professor’s input. Author: Lily Thompson Benefits of AI Python coding Automated code generation Most programmers waste a significant portion of their time brainstorming ideas to address coding problems. Some even rely on input from friends to generate ideas and develop existing solutions. While this method is effective for tasks with longer deadlines, it can be daunting when tackling urgent assignments. The obvious solution is to engage a professional to help with Python homework, but even then, you need to have an idea of what you want to solve. Just as essay writers for hire for student papers assist learners with academic projects, professionals in coding can provide valuable guidance for technical assignments. AI helps you toy around with your ideas and test the effectiveness of your original solutions. Even when working with a professional coder, you can generate an original solution to gauge the experts’ prowess. Besides, you can generate automatic codes with AI to expand your knowledge base. Automated bug detection Once you’ve generated your code, AI becomes even more useful. You can rely on this technology to detect bugs and possible solutions. AI-powered coding tools can help you analyze your code for problematic patterns and bugs. The tools also recommend improvement measures for debugging. Besides, AI tools [...]

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Performance Testing and Profiling in Python Applications

Software Testing Magazine — Fri, 26 Apr 2024 15:18:12 +0000

In the dynamic field of software development, where efficiency can be the difference between success and failure, Python stands out for its simplicity and broad applicability. However, achieving peak performance in complex production environments often requires meticulous tuning. This comprehensive guide delves into the essential practices of performance testing and profiling, which are indispensable tools for Python developers aiming to optimize their applications for maximum efficiency. Understanding Performance Testing and Profiling in Python Important Ideas and Definitions Performance testing looks at how well an app can work under expected load conditions, making sure it meets the standards for speed, response, and stability. Profiling goes along with speed testing because it gives you a full picture of how an application works while it’s running and shows you exactly where in the code slowdowns or problems happen. This difference is important because performance testing checks how long an app can run and how well it can grow in real-life situations that are simulated. Profiling, on the other hand, gives you more information about specific code problems and performance issues. Importance of Performance Optimization in Python Optimizing the performance of Python applications is crucial for several reasons. Efficient applications enhance user satisfaction by providing faster responses and more stable experiences. Additionally, they reduce operational costs by minimizing the computational resources needed, thereby allowing a business to scale more effectively without a proportional increase in hardware investments. By improving resource efficiency, optimized applications can support a larger user base or handle more transactions with [...]

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Pythonic Strategies for Ensuring Software Quality Assurance

Software Testing Magazine — Fri, 15 Mar 2024 14:33:03 +0000

Software quality assurance (SQA) plays a critical role in the software development lifecycle. With the rise in the adoption of agile methodologies, testing, and quality assurance are increasingly being interwoven into the development process rather than treated as a separate downstream phase. This allows issues to be caught and fixed earlier, reducing costs and time to market. Python has emerged as a popular language for QA and test automation due to its flexibility, scalability, and extensive ecosystem of testing tools and frameworks. Python allows testers and QA professionals to write tests that are easy to read and maintain. Its dynamic nature also makes it easy to quickly write tests without a lot of boilerplate code. Python can be utilized for API testing, UI testing, unit testing, integration testing, and more. The language also enables scalable test automation and continuous integration pipelines. This article provides an overview of various Pythonic strategies and best practices for building comprehensive testing and QA processes to ensure software quality from the start. We’ll explore how Python’s unique capabilities allow developers and testers to create robust validation of software functionality and business logic throughout the development lifecycle. Write Tests Early in Development Writing automated tests early in the development process, known as test-driven development (TDD), is a key Pythonic practice for ensuring software quality. TDD involves first writing failing test cases that define the desired functionality, and then writing the minimum amount of code needed to pass those tests. TDD results in code that is more [...]

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Mock Python Mocking and Testing Library

Software Testing Magazine — Mon, 05 May 2014 16:07:51 +0000

In unit testing, you use mocks to simulate the behavior of other code parts in controlled ways. mock is a Python library for testing that allows you to replace parts of the system under test with mock objects and make assertions about how they have been used. mock has been integrated the Python standard library since version 3.3 under the name unittest.mock. The mock library provides a core Mock class that removes the need to create stubs. After performing an action, you can make assertions about which methods / attributes were used and arguments they were called with. You can also specify return values and set needed attributes in the normal way. mock also provides a patch() decorator that handles patching module and class level attributes within the scope of a test, along with sentinel for creating unique objects. Mock is based on the ‘action -> assertion’ pattern instead of ‘record -> replay’ used by many mocking frameworks. The documentation of mock is really well written with a user guide that provides multiple examples on how to use this library in your python unit tests. It goes beyond being a simple documentation of the tool and provides valuable material on how to do mocking in python with plenty of code. Read the full mock documentation on https://www.voidspace.uk/python/mock/

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Understanding Python Unit Testing

Software Testing Magazine — Tue, 07 Jan 2014 21:45:47 +0000

It might seem strange that software developers still have trouble understanding. In this article, Jeff Knupp tries to describe what unit testing is, why it is useful and how you should perform unit testing for Python code. Jeff Knupp starts by making a difference for his article between testing, which he considers as an automated task, and “manual testing” that he classifies in the debugging category. He also defines the “unit” as an isolated portion of code. Then he explains how to use unittest, the Python’s built-in unit testing framework. He then tries to answer the question “Why is testing a valuable use of my time?” and gives four reasons: * Testing makes sure your code works properly under a given set of conditions * Testing allows one to ensure that changes to the code did not break existing functionality * Testing forces one to think about the code under unusual conditions, possibly revealing logical errors * Good testing requires modular, decoupled code, which is a hallmark of good system design Read the complete article on http://www.jeffknupp.com/blog/2013/12/09/improve-your-python-understanding-unit-testing/

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Python Unit Testing with pyVows

Software Testing Magazine — Wed, 18 Sep 2013 16:57:38 +0000

PyVows is an open source tools that provides asynchronous behaviour driven development for Python. PyVows allows executing the unit tests in parallel when it makes sense, and sequentially when there are dependencies. In this blog post, Michael Herman presents how to use pyVows for unit testing your Python code. The blog post explains how to test URL mappings, view functions, templates and the Django template context. For every element, there are code examples that can be grabbed from github. Other content discussing the usage of pywows is available from the same blog. Read the complete blog post on http://www.realpython.com/blog/python/unit-testing-with-pyvows-and-django/

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Unit Testing Python Code

Software Testing Magazine — Wed, 17 Apr 2013 18:08:53 +0000

This chapter from the “Hitchhiker’s Guide to Python” provides recommendations on how to perform unit tests in Python and presents the unitest and doctest modules. It also lists the tools that are available to perform unit testing in Python. Amongst the general rules of testing that are provided for Python, you can find: * A testing unit should focus on one tiny bit of functionality and prove it correct. * Each test unit must be fully independent. Each of them must be able to run alone, and also within the test suite, regardless of the order they are called. The implication of this rule is that each test must be loaded with a fresh dataset and may have to do some cleanup afterwards. * Learn your tools and learn how to run a single test or a test case. Then, when developing a function inside a module, run this function’s tests very often, ideally automatically when you save the code. * Always run the full test suite before a coding session, and run it again after. This will give you more confidence that you did not break anything in the rest of the code. * If you are in the middle of a development session and have to interrupt your work, it is a good idea to write a broken unit test about what you want to develop next. Read the complete resource on https://python-guide.readthedocs.org/en/latest/writing/tests.html

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