Software testing unit testing tutorials and videos

Why Duplicate Unit Tests Are Undermining Test Quality in the Age of AI

Software Testing Magazine — Tue, 18 Aug 2026 16:54:48 +0000

Like code, you can ask AI to generate your unit tests. But what about the quality of the resulting scripts. Who is going to check the coverage of the resulting to these automated tests? This article discusses the issue of ensuring that every AI-generated unit test contributes unique value is becoming the real challenge. Author: Eli Lopian The Overlooked Form of Duplication For decades, software engineering has embraced a simple principle: duplicate code is technical debt. Developers invest significant effort in identifying repeated logic, refactoring common functionality, and ensuring that each piece of production code has a single, maintainable purpose. Modern development tools even help identify duplication automatically because the cost of redundant code is well understood. Curiously, the same level of scrutiny is rarely applied to automated tests. As software projects mature, unit test suites often grow into thousands of test cases. Each new feature introduces additional tests, bug fixes result in regression tests, and developers frequently use existing tests as templates for writing new ones. More recently, AI-powered coding assistants have made generating unit tests almost effortless. Within seconds, developers can produce dozens of tests that compile, execute successfully, and increase code coverage. At first glance, this appears to be a tremendous advantage. Larger test suites suggest better protection against defects, while higher coverage percentages imply more thoroughly tested software. Yet beneath these encouraging metrics lies an increasingly common problem. Many automated test suites contain substantial numbers of duplicate tests—tests that repeatedly validate the same behavior while contributing [...]

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AI Can Generate Unit Tests But Who Reviews Them?

Software Testing Magazine — Tue, 30 Jun 2026 14:22:49 +0000

As AI makes test creation nearly effortless, software teams face a new challenge: determining which tests actually improve quality and which simply add noise. Author: Eli Lopian For years, software teams focused on a simple goal: write more automated tests. The industry invested heavily in testing frameworks, mocking libraries, CI/CD pipelines, and code coverage tools. The logic was straightforward. More tests meant fewer defects, safer releases, and greater confidence when changing code. That strategy worked. Modern applications often contain thousands of automated tests. Builds run on every commit. Coverage reports have become a standard quality metric. Automated testing is now a fundamental part of software development. But AI is changing the equation. Today, developers can generate dozens of unit tests in seconds using AI coding assistants. What once took hours can now happen almost instantly. As a result, the challenge is no longer creating tests. The challenge is understanding whether those tests actually provide value. A generated test may compile, execute successfully, and increase code coverage. Yet none of those things guarantee that it improves confidence in the software. That raises an important question: who reviews the tests? The New Bottleneck The software industry has spent years solving the problem of test generation. It may now need to focus on test evaluation. Traditional testing metrics offer only part of the picture. Code coverage tells us which lines of code were executed. Build reports tell us whether tests passed. Test counts tell us how many tests exist. What these metrics do [...]

The post AI Can Generate Unit Tests But Who Reviews Them? first appeared on Software Testing Magazine.

Beyond Unit Tests: Using AI to Find Secret Failures in Distributed Systems

Software Testing Magazine — Mon, 18 May 2026 16:22:55 +0000

As distributed systems are too complex for deterministic testing, AI can help. In this article, Naveen Prakash proposes an approach based on the ideas of chaos engineering and AI-assisted testing. The focus shifts from testing individual pieces to understanding what happens when many services run together under unpredictable conditions. Author: Naveen Prakash, QA Automation Architect & AI Testing Practitioner Knowing the Issue Imagine getting through an inventory check and somehow ending up with ‘never updated’ stock levels. A wrong inbox receives confirmation of an order. You run your unit tests — everything passes. Nobody’s broken. That’s the flavor of failure that makes these problems so hard. Because the bug’s not planted anywhere. It’s in the links. Modern systems are a tangle — databases, caches, messaging queues, microservices, API gateways, all talking to each other in a constantly shifting dance. A single user request might touch six or seven of these before it resolves. Tests usually check each piece in solitude, which is fine until you realise production does not run in solitude. Things slip through the cracks not because any single service broke, but because nobody thought to test what happens when two seventy-per-cent-correct behaviours land at the same time. Cache inconsistencies, retry amplification, async message reordering — the usual suspects. Gartner research suggests a large chunk of distributed system failures trace back to exactly these. Launching CLSSM The above model Cross-Layer Synthetic Scenario Modeling (CLSSM) can be used to ‘Twitch’ this. Perhaps it’s better to think of CLSSM as a [...]

The post Beyond Unit Tests: Using AI to Find Secret Failures in Distributed Systems first appeared on Software Testing Magazine.

Best Practice for Property-Based Testing

Software Testing Magazine — Wed, 03 Dec 2025 17:03:05 +0000

This talk aims to introduce property-based testing, why you may want to use it, and real-life use cases for including it in code bases. Property-based testing is an approach to testing that involves specifying statements that should always be true (for example, reversing a list twice will give the original result) rather than relying on specific examples. This presentation provides an introduction to property-based testing, why you would want to use it, and how you might apply it in the real world. How do you unit test a function? Typically, one might identify different sets of inputs (for example, for an integer you may have sets for positive, negative, and zero) and write a single unit test using a single example from each set. Can you be sure your code is correct for the whole set? Maybe it only works for that particular example? Here are the steps used to perform property-based testing: Identify properties (invariants) Think about rules that should always hold true for your function or system. Example: For a sorting function, the output should always be in non-decreasing order. Choose a property-based testing framework Popular tools: Hypothesis (Python) QuickCheck (Haskell, Erlang, Elixir) ScalaCheck (Scala) jqwik (Java) Write property tests instead of example tests Instead of writing assert sort([3,1,2]) == [1,2,3], you write: “For any list of integers, sorting it should produce a list where each element is ≤ the next.” Let the framework generate inputs The tool automatically produces random inputs (including edge cases like empty lists, [...]

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Open Source Database Testing Tools

Software Testing Magazine — Thu, 26 Sep 2024 08:00:55 +0000

Database testing is one of the areas that might have the smaller number of open source tools. The programming languages have many xUnit tools and mocking frameworks, but this is not the case for databases. This article provides a list of open source tools that can be used to perform unit, load and security testing on several relational (MySQL, Oracle, SQLServer, etc.) and NoSQL databases. This situation might be the results of the fact that most of the main relational databases are commercial tools that come with an infrastructure already provided by the vendors. The current rise of the NoSQL databases and the many forks that have been created from the original MySQL open source relational database might however change this situation in the future. The tools covered in this article are Database Benchmark, Database Rider, DBChaos, DBKover, dbstress, DbUnit, DB Test Driven, GODBT, HammerDB, JdbcSlim, JdbcRunner, JDBDT (Java DataBase Delta Testing), Mindleaf, MyTAP, NBi, NoSQLMap, NoSQLUnit, pgTAP, pstress, ruby-plsql-spec, SeLite, simple-db-tester, Spring DBUnit, SQLancer, sqlmap, tSQLt, Tsung, TypeORM Seeding, utPLSQL. Updates September 26 2024; added DBChaos, SQLancer, TypeORM Seeding February 14 2023: added DBKover, GODBT, pstress, simple-db-tester May 5 2020: added JdbcRunner, Mindleaf, MyTAP, pgTAP, Spring DBUnit January 23 2017: added JDBDT (Java DataBase Delta Testing), Database Rider, dbstress, NBi July 18 2016: added JdbcSlim, ruby-plsql-spec, Tsung, utPLSQL December 1 2016: added SeLite Database Benchmark Database Benchmark is an open source .NET tool designed to stress test databases with large data flows. The application performs two main test scenarios: [...]

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How to Choose the Right Name for Unit Tests

Software Testing Magazine — Mon, 22 May 2023 15:44:29 +0000

As unit testing is often the first quality activity targeting code during the software development lifecycle, this article considers several examples of effective approaches to the naming of unit tests. Author: Nataliia Syvynska, TestMatick, https://testmatick.com/ Unit testing is an essential part of software development that helps ensure code quality and catch bugs before they become bigger problems. However, writing effective unit tests requires more than just writing code that checks for the expected output. One often overlooked aspect of unit testing is the importance of naming tests correctly. A well-named unit test can communicate its purpose clearly, make it easier to understand the functionality being tested and help organize tests in large codebases. That is why in this article we will consider several examples of effective approaches to the naming of unit tests. By following these guidelines, you can improve the readability and maintainability of your unit tests, making unit testing an even more valuable activity in your software development process. First of all, let’s take a look at some fundamental guidelines for test naming: Be descriptive: it should be clear and easy to understand what the test is verifying. Use a naming convention: such as prefixing the test name with “test” or using camelCase. This makes it easy to identify unit tests and also helps automated testing tools locate and run them. Keep it short: a long name can make it difficult to read and understand the purpose of the test. Use meaningful names that reflect the functionality being tested. Avoid [...]

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Software Testing Tools in Visual Studio

Software Testing Magazine — Mon, 01 May 2023 15:04:02 +0000

This presentation shows how you can take advantage of the most recent additional to the unit testing tools in Visual Studio to build more reliable apps. Besides Visual Studio own tools, you can also run unit tests in Visual Studio by using third-party test frameworks such as NUnit, Boost, or Google C++ Testing Framework, depending on your programming language. The unit test tools of Visual Studio include: Test Explorer — Run unit tests and see their results in Test Explorer. You can use any unit test framework, including a third-party framework, that has an adapter for Test Explorer. Microsoft unit test framework for managed code—The Microsoft unit test framework for managed code is installed with Visual Studio and provides a framework for testing .NET code. Microsoft Native Unit Test Framework — The Microsoft Native Unit Test Framework for C++ is installed as part of the Desktop development with C++ workload. It provides a framework for testing native code. Google Test, Boost.Test, and CTest frameworks are also included, and third-party adapters are available for additional test frameworks. For more information, see Write unit tests for C/C++. Code coverage tools — You can determine the amount of product code that your unit tests exercise from one command in Test Explorer. Microsoft Fakes isolation framework — The Microsoft Fakes isolation framework can create substitute classes and methods for production and system .NET code that create dependencies in the code under test. By implementing the fake delegates for a function, you control the behavior [...]

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Does TDD Lead to Good Design?

Software Testing Magazine — Mon, 07 Dec 2020 15:49:34 +0000

“TDD is a design tool.” That is what the presenter has said for years. But not anymore. After working with different teams and in different organizations, and also carefully inspecting how he works, he changed his mind about the role of Test-Driven Development (TDD) in software design. This talk presents the pros and cons of the two main styles of TDD when it comes to software design, discusses why some software developers can test-drive well-crafted code while others can’t, and also explains how to reason about software design decisions. Video producer: https://devternity.com/

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