Systems | Development | Analytics | API | Testing

This article originally appeared on DevPro Journal. We’re sharing it here for our audience who may have missed it. For years, APM tools were everyone’s go-to solution for understanding how software behaved in production. And for a time, they worked, because architecture was simpler. Developers owned the backend, the frontend, and the data layer. Everything lived inside a monolith. If something went wrong, they could trace it through their codebase and fix it.

Testing has evolved far beyond scattered spreadsheets and disconnected tools. Yet even with modern platforms in place, teams still run into bottlenecks, especially when fundamental tasks like test case creation are handled manually. It involves combing through acceptance criteria, writing out each step, and reviewing everything for gaps. Repeating that across multiple user stories quickly drains time and slows progress – it’s repetitive, time-intensive, and prone to inconsistency.

Artificial Intelligence(AI) has recently become a hot topic across industries transforming sectors like finance, healthcare, education and research. The two of its subfields are Generative AI and Machine Learning(ML), but both of these terms are often confused for one another. we will explore the difference in purpose, techniques and capabilities and tools like Keploy’s GenAI-powered testing platform makes big difference in software testing.

Outsourcing quality assurance (QA) used to be about saving money. In 2025, you will have to build resilience into the release pipeline.

SAP data scrambling is critical to protect business data. Over 90% of Fortune 500 companies manage their business operations using SAP systems. Each company typically maintains many non-production environments containing sensitive data. But when it comes to protecting SAP data, too many enterprise leaders believe the myths. To truly protect SAP data at your enterprise, you need to learn the truth and you need to adopt best practices. In this blog, we’ll share exactly that.

In today’s fast-moving tech world, microservices have become the backbone of modern applications. But with flexibility comes complexity, especially when it comes to testing. Unlike monolithic apps, testing microservices requires a different approach. Each service runs independently, and making sure everything works smoothly together is both important and challenging. In this blog, we’ll walk through what microservices are, why testing them matters, and how to approach them step by step.

When we first started using Playwright for automated testing, the built-in test runner and test reports seemed fine. It showed passed tests and failures, which worked for small projects. But as our large test suites and CI/CD pipelines expanded, the default test runner reports became limiting especially when analyzing detailed results from each test run, the default test runner reports became limiting—especially when analyzing large-scale test results.

The digital world waits for no one. Releasing software is no longer a marathon; it's a series of high-speed sprints where flawless execution is the only acceptable outcome. How can teams deliver at this relentless pace without sacrificing the quality that users demand? The answer lies in intelligent automation, a strategic imperative for any modern development lifecycle.