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

What changed for the customer, and what was the final outcome?

They can now run large test suites quickly, execute at scale, and get consolidated reporting — all major improvements from where they started. The journey continues, but today they’re also using AI-driven test generation and tracking ROI, with Katalon adding features based on their needs. — Coty Rosenblath, Chief Technology Officer at Katalon Follow Katalon for more insights in our series!

The Future of Digital Experience is Autonomous, so is Testing

The digital economy has upgraded from simple transactional interactions with users. Now consumers demand the Autonomous Digital Experience (ADE) – the customer journey is driven by predictive, self-learning systems, which is essential for competitive success. This is driven by Predictive Personalisation, which uses machine learning to predict personalised affinity and intent of user actions, delivering personalised content, products and messages in real-time.

How To Use Copilot In Software Testing: A Practical Guide For Testers

Software testing is critical in assessing the quality of apps, testers oftentimes have to deal with limited resources when it comes to creating tests, as well as repetitively creating tests for all feature coverage. These factors lead to a significant reduction in both the speed of development and efficiency in the testing process.

Express Scripts by Evernorth Makes Data HIPAA-Compliant Overnight | Perforce Delphix

Express Scripts by Evernorth — a health services company serving 180 million members and processing 1.4 billion prescriptions annually — faced long lead times for HIPAA-compliant data. Its software engineering, data scientist, and machine learning teams needed fresh, readily available data, but the original system Express Script used took two weeks for its refresh masking cycle.

ROI of Digital Twin Testing: Cut Testing Costs by 50%

When engineering leaders review their cloud bills, they often focus on production costs—the infrastructure serving real users, processing real transactions, generating real revenue. But there’s a shadow cost lurking in every cloud environment that often goes unnoticed until it becomes painful: non-production infrastructure.
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Peeking Under the Hood with Claude Code

Claude is one of the go-to AI-native code editors for developers. Because it's a simple chatbot interface housed inside a familiar CLI, it provides a pretty smooth path between traditional IDEs and agentic AI. But what's actually happening behind the scenes when you ask it to write code, generate a test, or debug an issue? Who and what is it talking to behind the scenes? Can I prevent data leakage or do I need to add another layer to my tin foil hat? To answer these questions, I used proxymock to inspect the network traffic flowing from the Claude IDE.

Moving Our Observability Data Collector from Sidecars to eBPF

For years, the Kubernetes sidecar pattern has been a practical way to capture observability data. Running a collector alongside each application pod gave us deep visibility into traffic, including full request and response payloads across supported protocols. However, as cloud-native environments have grown more complex, the limitations of sidecars—such as resource overhead, operational complexity, and scaling challenges—have become more apparent.