Systems | Development | Analytics | API | Testing

At SmartBear, we celebrate innovators in software testing who make a real impact on their teams. The Automation All-Star Award celebrates the pioneers using SmartBear Zephyr’s no-code automation to achieve transformative results. We’re thrilled to share the stories of this year’s winner, RAC Motoring Services, and our fantastic finalists: Capgemini, Infor PSSC, Pepper Money, and the Center for Sustainable Energy (CSE).

In an era when artificial intelligence is transforming every industry, your Oracle data has never been more valuable or more vulnerable. Data trust is no longer optional. It’s foundational.

When searching for ETL pipeline best practices, you will find some common themes: ensuring data quality, establishing consistent processes, and automating out repetitive tasks. There’s a reason these are recommended over and over: they help establish reliable, efficient, and scalable workflows. But one thing that isn’t often emphasized is the importance of implementing consistent, scalable compliance efforts — specifically by using data masking.

With an increasingly competitive market, ensuring software quality is essential for your business’s success. QA governance and strategy are crucial to ensure that processes are efficient, consistent, and aligned with business objectives. Managing these factors can be challenging, especially when teams deal with multiple tools, a lack of visibility about test progress, and maintaining compliance.

This article originally appeared on SD Times on May 30, 2025. We’re sharing it here for our audience who may have missed it. The headlines are seductive: AI will replace developers. Coding is dead. Ship 10x faster with half the team. It’s the kind of hype that grabs attention and fuels confusion.

Due to artificial intelligence and Large Language Models, having streamlined data communication, easy access and easy discovery is now more important than ever. Many people are unaware of it, but the llms.txt file is very important and key to what happens in the machine learning and web sectors. With this plain text file, websites and platforms can interact with LLMs, giving metadata that can impact the way AI indexes, interprets and generates their content.

Ever deployed code only to watch everything crash? We’ve all experienced that sinking feeling ,which is exactly why testing matters so much. While most developers understand that testing is vital, two important approaches : unit testing and regression testing are frequently confused, despite serving completely different functions. In this blog, we’ll clear up the confusion, examine their core methodologies, and how tools like Keploy can automate and enhance your testing processes.

Developing high-quality products means carefully defining and tracking both functional and non-functional requirements (NFRs). But what exactly are non-functional requirements and how can you best manage them? This guide will help you understand the difference between functional and non-functional requirements with real-world examples. It also covers best practices for writing them and discusses how to track and manage non-functional requirements for greater efficiency.

In the field of software quality assurance, there are two types of testing often referenced, smoke testing and regression testing. While they are both vital to software quality, each has its own unique functions and overlaps in the software development cycle. This post explores the differences between smoke testing vs regression testing, and when and how to effectively implement each.