Systems | Development | Analytics | API | Testing

According to SmartBear’s Closing the AI Software Quality Gap study, 93% of teams are already using AI to generate code. The same study found that 60% expect AI to produce nearly half of all code within the next year. This shift in development velocity is already impacting software testing and quality. Most teams say application quality is suffering, and 60% have experienced quality issues in the past year because development is moving faster than testing can keep up.

While AI accelerates development velocity by a factor of ten, a critical consequence remains: testing hasn’t kept pace. According to SmartBear research, 70% of software professionals report that their application quality has already degraded due to AI-accelerated development. Even more concerning, 60% have experienced quality issues in the past year as development velocity outstrips testing capacity.

Organizations with AI mandates face a fundamental choice in test automation: adopt AI-native testing tools like SmartBear Reflect or use AI coding tools to accelerate adoption of code-based frameworks like Playwright. Reflect is a cloud-based, no-code test automation platform built around accessibility and speed. Playwright is Microsoft’s open-source, code-based testing framework built for flexibility and engineering control.

Teams using TestComplete face a common problem: one small test change can produce a wide set of modified files, and not all of them deserve the same level of scrutiny. The fix is not to review everything equally – it is to classify TestComplete artifacts by risk, then standardize how your team reviews, stages, and merges them. This article outlines this process and offers best practices for using Git effectively with TestComplete projects.

Testing software at scale has always been a race against change. Then, AI-coding turned what was once a challenge into a crisis: rapid development cycles accelerated by AI have made it impossible to maintain comprehensive test coverage and catch issues before they impact users. In SmartBear’s Closing the AI Software Quality Gap Study, 60% of software experts told us they experienced quality issues as development outpaces testing.

AI-accelerated development has fundamentally changed how software is built, and across the industry, its impact on quality is already measurable. In SmartBear’s Closing the AI software quality gap study, we found nearly 70% of software professionals report application quality is declining as AI speeds up code generation, with development velocity increasingly outpacing teams’ ability to test effectively.

In 2026, APIs have moved far beyond simple integration points. They’re now strategic business assets powering AI transformation, microservices architectures, and multi-cloud ecosystems. But a critical challenge threatens to undermine digital initiatives: the fragmentation of API testing. As organizations rush to deliver faster, they’re discovering that their testing infrastructure – cobbled together from disparate tools and disconnected processes – has become the bottleneck.

There’s a tension building inside most engineering organizations right now, and not many people are talking about it openly. AI has given development teams an extraordinary gift: the ability to build faster than ever before. Features that once took days can be prototyped in hours. Applications that required large teams can now be scaffolded by a handful of engineers with the right tools. By almost every measure of development velocity, we are living through a remarkable moment.

Digital transformation rarely happens in a clean, technical environment. Most organizations aren’t starting from a blank slate – you’re operating across a mix of legacy desktop applications, internal web systems, custom-built interfaces, and business-critical workflows that must remain stable while modernization continues around them. The central challenge is whether that automation can remain reliable as underlying technologies evolve.

If you’ve used Claude Code, GitHub Copilot, Cursor, or any coding agent, you already know the feeling. You describe what you want in plain language, the agent figures out the steps, and you watch it work. When something goes wrong, it backs up and tries a different approach. Reflect now brings that same agentic workflow to test automation. Through the SmartBear MCP server, any coding agent that supports MCP can connect to Reflect and build tests from high-level objectives.