Unlike traditional testing methodologies - that rely heavily on pre-scripted tests and automated tools - Human-Centric Exploratory Testing (HCET) is a dynamic and flexible approach that leverages creativity and critical thinking, allowing interaction with the software in ways that automation simply cannot replicate. Below, we explore Human-Centric Exploratory Testing, its benefits, challenges, how to mitigate them, and the best practices for successfully implementing it.

It's well known that creating API test cases can be time-consuming and repetitive. Manually adjusting web service requests and verification steps is not only tedious but also prone to errors, especially when dealing with complex systems. Today, we’re thrilled to announce a game-changing feature in Katalon Studio that will revolutionize your testing workflow: AI-powered API test generation from OpenAPI/Swagger specifications.

Yellowfin 9.12 is an extensive release that incorporates a large number of bug fixes, additional data connectors, and customer requested enhancements. The biggest changes in this release focus on Export and Broadcast capabilities. Yellowfin already boasts exceptional reporting capabilities, and 9.12 adds additional features and performance improvements in this area. Yellowfin also has the ability to connect to just about any source of data.

Quality gates are just what they sound like: They ensure the delivery of high-quality software by serving as quality milestones (or “gates”) during various stages of the software development lifecycle (SDLC), preventing bad code from passing through. Here, we explain what are quality gates, how they work, and how to implement them using static analysis. Read along or jump ahead to the section that interests you most: Manage Your ci/cd pipeline with Static Analysis.

Payments are probably the most important part of your ecommerce store. You can only run it if you have a way to collect money and then reuse it to create more products and market your store. This is why you need to meticulously choose the best payment tools. They need to have no downtime so you can accept payments any time anywhere and they also need to help you maximize payments so you get the maximum revenue from your customers.

Modern edge computing is transforming industries including manufacturing, healthcare, transportation, defense, retail, energy, and much more—pushing data management to far-reaching data sources to enable connected, low latency operations and enhanced decision making. These new use cases shift workloads to the left—requiring real-time data streaming and processing at the edge, right where the data is generated.

Are you seeking to ensure robust communication between APIs and applications? Contract testing plays a crucial role in ensuring this robust communication. The key lies in the API contract. Serving as a definitive guide, API contracts align expectations, prevent errors, and cement the reliability of software interactions. This article breaks down the ‘how’ and ‘why’ of API contracts, including creation, benefits, and maintenance strategies for developer and consumer success.

While cloud computing adoption continues to accelerate due to its tremendous value, it has also become clear that edge computing is better suited for a variety of use cases. Organizations are realizing the benefits of processing data closer to its source, leading to reduced latency, security and compliance benefits, and more efficient bandwidth utilization as well as supporting scenarios where networking has challenging constraints.

At the latest WWDC, Apple unveiled an exciting new feature: dynamic tinting for app icons. This update calls for developers to adjust their app icons to support this functionality, ensuring a more visually consistent experience across iOS. This change is significant for maintaining a seamless user interface, allowing app icons to adapt to various system-wide themes, including dark mode, and providing a cohesive look throughout the operating system.

This article was originally published on The New Stack on June 10, 2024. Retrieval augmented generation (RAG) has emerged as a leading pattern to combat hallucinations and other inaccuracies that affect large language model content generation. However, RAG needs the right data architecture around it to scale effectively and efficiently.