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Score being shown at a baseball game More Than a Score: Taking a Deeper Dive into Your Metrics

One key benefit of metrics is that they can be measured using a standard process; we can explain the numbers, and leadership can understand what that means. The downside is that it is only a measurement, so issues can easily hide until they become problems, and great work can also go unrepresented. Sporting events are a great example: The end score tells you who won, but not the details of the game. We need to look deeper.

Nels Hoenig's picture Nels Hoenig
Pyramid in Egypt Inverting the Test Automation Pyramid

A growing company was tasked to develop a test automation program from scratch, change its coding practices, and build a continuous testing toolchain. Martin Ivison details how they did it, including realizing that implementing the traditional test pyramid wasn't going to work—it would have to be turned upside down. They found out that small is beautiful, cheap is good, and cultural change matters.

Martin Ivison's picture Martin Ivison
Artificial intelligence bot AI-Driven Test Automation and Your Future

Many software testers are lamenting the impending demise of their jobs thanks to artificial intelligence. But Jon Hagar thinks there's no need to panic just yet. Here, he details some capabilities he's seen in AI, relates how these can be used in software testing, and explains why he thinks most people don't have to worry—although he also explains who should! As usual, it comes down to a willingness to learn new things.

Jon Hagar's picture Jon Hagar
Computer screen showing clean code Clean Coding Practices for a Scalable Test Automation Framework

Many organizations are looking to expand their automation abilities by designing and developing test automation frameworks. However, we often abandon good coding practices in favor of working as fast as possible. We need to treat this project like any other application development project. Here are three of the most important clean coding practices to keep in mind in order to make a scalable test automation framework.

Sumon Dey's picture Sumon Dey
Team members fitting puzzle pieces together Whole-Team Testing for Whole-Team Quality

Whole-team testing means the whole team understands and participates in testing, using testing education as a tool to support quality efforts. And to be able to support testing in a meaningful way, team members must experience how testing is done by professional testers. Understanding skilled testing can help non-testers realize what quality criteria should be there and what elements of a product contribute to great quality.

Lalit Bhamare's picture Lalit Bhamare
Tester holding up a pair of eyeglasses Testing What You Can’t See: Risk Blindness in Coverage Models

The way we think about what necessitates test coverage being “complete” influences how we test and the cases we create. After all, you wouldn't design tests for situations that don't occur to you—and you can't test what you can't see. It's time to take off the blinders. Here's how you can find where the bugs in your products are occurring, and then adjust your strategy to pinpoint them.

Matthew Heusser's picture Matthew Heusser
Software package installer icon Brew vs. Pip: Which Package Installer Should You Use?

A command-line package installer is a handy tool that installs your desired software package without a fancy UI, yet it often proves to be more effective than some tools integrated into expensive IDEs. Brew and Pip are two of the more popular options for package installers when using the script language Python. But what’s the difference between them, and which makes more sense for your use? Here’s an introduction to Brew and Pip for testers.

László Szegedi's picture László Szegedi
Two paths going through the woods Taking the Negativity out of Negative Testing

Everyone on the software team has the same goal of delivering the best product they can, so letting testers discover bugs is always good—the more bugs found, the better! But misconceptions often lead to testers getting the bad rap of "breaking" the software. It's a tester's job to think like a user. Developers and stakeholders might call that negative testing, but the result is a better product, and that’s all positive. Let's change the way we talk about testing.

Jessica Lavoie's picture Jessica Lavoie
Close-up of computer keyboard Testing AI Systems: Not as Different as You’d Think

AI-based tools have transformed from a vague, futuristic vision into actual products that are used to make real-life decisions. Still, for most people, the inner workings of deep-learning systems remain a mystery. If you don’t know what exactly is going on while the input data is fed through layer after layer of a neural network, how are you supposed to test the validity of the output? It’s not magic; it’s just testing.

Kerstin Kohout's picture Kerstin Kohout
Eyeglasses bringing data on a computer screen into focus Finding the Information inside Your Data

Data analysts have to know a lot about diverse business areas so that our reports provide usable information, not just data. We can use this awareness of the value of information to merge different data sets in order to answer new questions, and even help our users make better decisions. But in order to do this, we need to present not just the data, but the information value represented in that data.

Nels Hoenig's picture Nels Hoenig

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