Systems | Development | Analytics | API | Testing

Why Does Validation Testing Matter in Software Engineering?

Most software bugs could be traced to validation mistakes. Think about building an app on paper that has no bugs and is flawless, but when they release it to the end user, they keep encountering problems since the software never addresses their problem or fulfills their requirements due to poor data validation. This is where software testing and validation testing enter the picture. It's the activity of making sure the software you've built satisfies end-user expectations, not only technical requirements.

Overcoming the Challenge of Planning & Deploying AI

We know the role that AI can play in modern business, and the benefits it brings to employees and customers. But launching and sustaining a successful AI project remains a critical challenge for many organizations. Technology leaders across the globe are being tasked with using AI to drive business success, and it is becoming a vital pillar in reaching strategic goals.

Introducing Tosca's spring 2025 release: Your path to low-footprint test automation in the cloud

At Tricentis, we’re committed to pushing the boundaries of software testing, and our latest innovation, the spring 2025 release of Tosca, is a testament to that commitment. This update introduces a suite of advanced features for Tosca’s cloud deployment and is designed to elevate your test automation experience, further enhance authoring, and advance test automation and zero-footprint execution.

Future-proof your automation strategy with Xray Enterprise

The future of software development is fast, automated and constantly changing, so what you should be questioning is: “can my test automation strategy keep up?” Development lifecycles are sometimes cut short and the delivery is needed quicker - without a proper approach, your test automation strategy can become a bottleneck instead of an advantage. With this article, you’ll understand all the features Xray Enterprise brings to the table.

How To Build a Test Automation Techstack?

Embarking on the test automation journey can be exciting, and daunting, at the same time. It's exciting, because we all know how test automation translates into faster releases, fewer bugs, and most importantly, more bandwidth for QA teams to perform higher-value exploratory tests. It's daunting, because building a test automation tech-stack is full of unknowns: We wrote this article to answer those questions for you and simplify the process of embracing test automation.

3 Elements of a Forward-Looking Data and AI Strategy

“AI is inevitable, but is your data ready for all AI has to offer?” That was the unspoken question every keynote, panel, and hallway conversation sought to answer at the 2025 Gartner Data & Analytics (D&A) Summit. Gartner’s response was loud and clear: AI can drive incredible value, but without a good data foundation, it’s garbage in, garbage out.

Kotlin Apply and other Kotlin Scope Functions

Last week, we got a question from one of our users asking us how to use Kotlin Apply. Specifically, the reader wanted to know whether it was best to use the apply function in their Android application, or another of the many Kotlin scope functions. So we got to thinking: Why not write an article about the whole topic of Kotlin scope functions? After all, they’re awesome: they let us write readable, concise code in Kotlin, and work with an object without the need for repeated references.

Replicating Data from Oracle to BigQuery - Steps Explained

In a time where data is being termed the new oil, businesses need to have a data management system that suits their needs perfectly and positions them to be able to take full advantage of the benefits of being data-driven. Data is being generated at rapid rates and businesses need database systems that can scale up and scale down effortlessly without any extra computational cost.

The Apache Iceberg Avalanche: How the Open Table Format Changes the Face of Data Lakes

Data storage has been evolving, from databases to data warehouses and expansive data lakes, with each architecture responding to different business and data needs. Traditional databases excelled at structured data and transactional workloads but struggled with performance at scale as data volumes grew. The data warehouse solved for performance and scale but, much like the databases that preceded it, relied on proprietary formats to build vertically integrated systems.