Software Testing Articles & Tutorials: Load Testing, Unit Testing, Functional Testing, Performance Testing, Agile Testing, DevOps
A customer appears on the map, the address looks correct, and the page loads without errors. The test passes. But what happens when that customer sits exactly between two service areas? Which team receives the account? Does the answer change after someone edits a boundary?
AI-assisted code generation has changed the capabilities of organizations to produce code. In this article, Hélder Ferreira discusses the impact on software quality created by this new situation. The main problem is that this new code is often created by tools that lack the knowledge of the global context of both the application infrastructure and the process being automated.
QA work abroad runs on more than a laptop and a Wi-Fi signal. It depends on secure testing environments, reliable device access, protected data, and systems that keep collaboration steady across time zones. Build that foundation before you travel, and testing quality holds up no matter where you’re working from.
Production failures rarely arrive with a clean explanation. A release looks healthy in staging, then fails under real traffic. A background job works for most customers, then breaks for one segment. A service slows down after a deployment, but only when a specific dependency, payload shape, feature flag, or account path appears.
Security software that works perfectly in a controlled setting can quickly fall apart when used at scale. As organizations grow, their security solutions need to handle a huge increase in data, users, and connected devices. This puts a lot of pressure on software development and quality assurance teams.
When a company starts to blossom, adding tools to the mix for software teams is a typical move. A company may need code editors, test automation tools, cloud space, and updated project management software. But when a software team keeps adding tools without an overarching strategy in place for those tools, they might go overboard.
A fully green traceability matrix can make it look as though requirements are properly covered. Yet a link between a requirement and a test does not prove that the test actually verifies every required behavior. Requirements coverage gaps can remain when tests are incomplete, when a requirement evolves without its tests following, or when a test changes while its traceability link remains intact. This article proposes a simple way to detect them: first understand how they arise, then structure the review by comparing explicitly required behaviors with what the linked tests actually demonstrate.