Systems | Development | Analytics | API | Testing

Have you ever tried to ship a software update smoothly but suddenly encountered a few bugs that slowed down your entire DevOps cycle? Forgetting about these small issues can quickly turn them into big problems— time, money, and user trust. Software teams need fast, reliable testing, and manual methods can be inadequate. That’s why test automation has become a necessity, especially in high-velocity DevOps settings.

This is Chapter 2 of our journey of non-functional testing with Otto Perf. You can read Chapter 1 if you have missed it. Authors note The article is in the OctoPerf Blog pages Otto had previously got to the point where he had defined a set of non-functional requirements and risk assessed these with the programme team.

The “organic” evolution of a software tester should follow the organic evolution of an organization’s QA maturity. A tester can be conveniently imagined as an organism existing within a larger ecosystem (in our case, the organization). An organism does not exist in isolation. Rather, it responds to, adapts to, and evolves along with the environment in which it finds itself.

Shipping high quality apps requires thorough testing, and testing thoroughly requires teams to automate more of their process. Quality isn’t just about testing more – it’s about testing smarter. By optimizing your workflow, you create space to validate every critical part of your application efficiently and reliably. Full-scale quality comes from full-coverage testing.

Hiring a software testing company can be a good way to get more testing done without recruiting expensive, full-time QA engineers. However, through conversations with engineering leaders and developers, we frequently hear complaints about software testing companies bottlenecking release pipelines, missing apparent visual bugs, and highlighting non-issues as bugs.

Enterprises working in Microsoft Azure need to mask data, and they need to mask it quickly.Yet, it can be difficult to find Azure data masking methods that are both swift and secure. The wrong approach can lead to bottlenecks, compromised analytics quality, and significant regulatory risks.

In the evolving landscape of software testing, Behavior-Driven Development (BDD) has emerged as a powerful methodology for improving collaboration between technical and non-technical team members. However, the implementation of BDD tools like Gherkin and Cucumber often falls short of their intended purpose. Let's explore how to effectively use these tools and avoid common pitfalls.