Systems | Development | Analytics | API | Testing

Imagine downloading a new app, opening it for the first time, and, boom, it crashes. Chances are, you won’t give it a second try. You’ll uninstall and move on. That’s exactly how unforgiving today’s users are. In 2025, the difference between a five-star app and one that gets deleted in minutes can come down to something as tiny as a 0.05% dip in crash-free sessions.

The future of testing isn’t about replacing humans with AI. It’s about augmenting your team’s capabilities. Assistive AI tools can summarize logs, generate test cases, triage defects, and surface insights - all while keeping humans in control. This low-risk, high-leverage approach helps enterprise teams move faster, improve coverage, and focus human judgment where it matters most. Start small, measure impact, and treat AI as a test assistant - not a magic box.

By Erez Schnaider, Technical Product Marketing Manager, ClearML The number of AI applications are rapidly increasing, and it can be difficult to keep up. Every month brings a new protocol, LLM, or tool. In this environment, the true strength of a platform is measured not only by its core features but also by its extensibility and adaptability to change. Many platforms address this challenge by hosting OSS tools or exposing API connections.

Managing a large suite of automated tests, especially across multiple tools, can be overwhelming. The SmartBear MCP (Model Context Protocol) Server centralizes orchestration, monitoring, and prioritization so you can keep pipelines fast, reliable, and easy to manage. The demo video below shows MCP in action, and the sections that follow explain how each capability can help you get more out of both open-source and commercial testing tools. Explore these docs to learn how to get started.

It's 3 AM, and during the testing process, your mobile app crashed in the middle of high traffic. Your outdated test infrastructure is unable to mirror real-life scenarios fast enough, and serious problems go undetected. On the other hand, a competitor provides a seamless user experience as they embrace Cloud-Based Testing and gain unmatched flexibility of open devices, speed of finding defects with their rapid scalability.

Swift Composable Architecture (TCA) is one of the cleanest and most scalable ways to build iOS and macOS apps today. Created by Point-Free, it pulls together state management, side effects, dependency injection, and modular design into one consistent and predictable system. Whether we’re crafting a tiny feature or designing a full-scale app, TCA helps us write Swift code that’s easier to test, easier to work out, and a lot less painful to maintain over time.