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The latest News and Information on Software Testing and related technologies.

Cloud-Based vs On-Premise Website Monitoring: Which Approach Wins in 2026?

Website monitoring often feels like choosing between casting a net or wielding a spear. Cloud-based monitoring is your fishing net: you deploy it broadly, covering vast swaths of digital water, capturing issues wherever they occur. It’s automated, relentless, and covers every corner of your online presence, from global uptime to minute performance blips. On the other hand, on-premise monitoring is the spear – deliberate, targeted, and controlled.

Why performance validation is an infrastructure issue, too

At Datadog DASH in the spring, the Tricentis NeoLoad team met folks in all types of roles – developers, test engineers, CoE leads, and SREs – and one recurring theme we found was that SREs often didn’t know much about how performance validation happens at their companies. That’s a fair division of labor. Seemingly, the performance team’s work would be related but not mission-critical to the infrastructure team’s. But that’s not entirely true.

Test generation got free. Test governance didn't.

Over the past twelve months, something has shifted in how software teams think about quality. Claude Code, Cursor, and Playwright agents have made it genuinely easy to generate functional tests without deep automation expertise. The impact is showing up across two very different kinds of teams: Both paths lead to the same place: tests accumulate without governance, you get coverage without traceability, and there’s automation debt no one owns. But coverage numbers and quality are not the same thing.
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Two confident fixes missed this production bug

Every new signup posts a message to our Slack. The format is dull and reliable: Overnight this week one arrived like this: That trailing nothing was the entire incident. No error logs, no alerts. A returning user had signed up, our signup service had attached them to a tenant we deprovisioned back in December, and the only symptom in the whole company was a Slack message that ran out of words. Their company's email domain still pointed at the dead tenant, so instead of a fresh workspace they landed in a corpse: domain mapping still live, cloud infrastructure long gone. I'm on call this week, so I pulled the thread.

The Pod Was Cheaper. The Service Wasn't.

A smaller Kubernetes pod can lower allocation cost while completing less work. Green status codes and matching schemas can hide it. This walkthrough combines OpenCost allocation data with proxymock behavior and performance evidence. A candidate passes only when behavior and throughput hold while unit cost falls.

Diagnose Serial N+1 API Calls With Tempo + proxymock

One API request took 302 milliseconds. Nothing failed. CPU was mostly idle. The response was correct. The trace made the problem obvious: eight inventory calls, each waiting for the previous one. But the trace could not tell me why the application made eight calls, or whether changing their execution would preserve the response. It showed the shape of the wait, not the input that created it.

Practice what you Pact : Catch breaking API changes before production in the SmartBear MCP

There’s something satisfying about contract testing the contract-testing tool. The SmartBear MCP Server is the integration layer between AI coding assistants and the PactFlow API, so when we decided it needed Pact consumer tests of its own, we were subjecting our own code to the same standards that we recommend.

MCP is the new API: Why your team needs to start rethinking integration testing

For decades, APIs have been the universal handshake of the digital economy. You want data? Call an endpoint. You want to trigger an action? POST a payload. APIs are simple, predictable, and everywhere. But in 2024, Anthropic introduced the Model Context Protocol (MCP), an open standard that dramatically changed how systems speak to one another. MCP enables AI agents to connect with external apps without custom code.

Katalon Named a G2 Leader for Both Mid-Market and Enterprise in Test Automation

Most test automation tools are sized for one kind of team. The ones a lean QA group can adopt inside a quarter tend to buckle when a compliance officer asks who approved a change and when. The ones large enterprises standardize on often need a dedicated platform team before anyone writes a single test. Anyone who has evaluated tooling knows the feeling of reading a glowing review and realizing it was written by someone at a company nothing like theirs.