Software professionals are looking to speed up testing without sacrificing accuracy or time, so once again, some of the most-read articles in 2020 were about risk coverage, test design, data analytics, AI-based tools, and Java. This year, our two most popular articles were about package installers for Python and a tool you may be misusing. For a shareable infographic, click here.

10. Risk Coverage: A New Currency for Testing 
By: Wolfgang Platz

In the era of agile and DevOps, release decisions need to be made rapidly—preferably, even automatically and instantaneously. Test results that focus solely on the number of test cases leave you with a huge blind spot. If you want fast, accurate assessments of the risks associated with promoting the latest release candidate to production, you need a new currency in testing: Risk coverage needs to replace test coverage. Read the article.

9. Using Decision Tables for Clear, Well-Designed Testing
By: Josh Giller

Decision tables are used to test the interactions between combinations of conditions. They provide a clear method to verify testing of all pertinent combinations to ensure that all possible conditions, relationships, and constraints are handled by the software under test. If you need to make sure your test cases cover all outcomes in a scenario, read on to learn how to use decision tables. Read the article. 

8. Applying Data Analytics to Test Automation
By: Harsh Vardhan

Testers gather lots of metrics about defect count, test case execution classification, and test velocity—but this information doesn't necessarily answer questions around product quality or how much money test efforts have saved. Testers can better deliver business value by combining test automation with regression analysis, and using visual analytics tools to process the data and see what patterns emerge. Read the article.

7. Testing AI Systems: Not as Different as You’d Think
By: Kerstin Kohout

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. Read the article.

6. Java for QA Automation Engineers: How to Learn?
By: John Selawsky

If you are a manual tester and want to be a QA automation engineer, learn Java and programming via these 10 steps. Read the article. 

5. 6 Ways Testers Can Add Value (Other Than Functional Testing)
By: Ajay Balamurugadas

Many testers spend their time doing functional testing and don't come out of this cocoon. But software testing is all about discovering quality-related information to assist stakeholders in making informed decisions, and there are multiple ways to discover information in addition to functional testing. Here are six actions that will help you add more value to your projects. Read the article.

4. JUnit vs. TestNG: Choosing a Framework for Unit Testing
Junaid Ahmed

There are multiple frameworks available for unit testing, and for any type of programming language. For Java developers, JUnit and TestNG are the most widely used. These frameworks are siblings and have the same test roots, and the debate over which is better is complex. Let’s look at how these two testing frameworks are different from each other, and which framework is better suited for your unit testing. Read the article.

3. The Shift-Left Approach to Software Testing
By: Arthur Hicken

The earlier you find out about problems in your code, the less impact they have and the less it costs to remediate them. Therefore, it's helpful to move testing activities earlier in the software development lifecycle—shifting it left in the process timeline. This article explores the shift-left methodology and how you can approach shifting left in your organization. Read the article.

2. Brew vs. Pip: Which Package Installer Should You Use?
By: László Szegedi

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. Read the article.

1. Why You Shouldn't Use Cucumber for API Testing
By: Byron Katz

Many people misunderstand the purpose of Cucumber. Because it seems to yield clearer, plain-language test scripts, testers want to use Cucumber as a general-purpose testing tool, including for API tests. But its true purpose is as a BDD framework. You may be thinking, what’s the harm? Here’s why it makes a difference—and why you should choose another tool for API testing. Read the article.