SmartBear released VisualTest, a new AI-powered automated regression testing tool that allows developers to easily catch visual defects and quickly confirm their website looks as designed, ensuring the best customer experience.
Automatically uncovering errors and highlighting inconsistencies using a robust comparison engine, VisualTest delivers component-level visibility alongside the automation processes for CI/CD, giving developers and testers visual accuracy and automation at scale. VisualTest also integrates with mobile and web application testing solution, BitBar, enabling teams to reduce context switching between visual, web, and real-device testing.
“Customer experience is critical for online businesses, especially now in a fiercely competitive environment,” said Joanna Schloss, Senior VP Product Marketing at SmartBear. “With frequent software updates common for SaaS and e-commerce companies, visual changes happen often and errors can frustrate users...It [VisualTest] is quick to deploy through self-service and easy to use, enabling developers to automate visual regression tests and correct problems before they get to the end user.”
With VisualTest, developers gain visibility into the design-and-build phase of the application with next-generation machine learning and AI engine tracking several types of visual changes. VisualTest ignores false positives while highlighting key differences, saving developers time and enhancing their applications. VisualTest essentially replicates the human eye for finding and catching visual errors that are not always detected through traditional functional or end-to-end testing. Using AI to run visual tests allows developers to focus only on important changes or differences. This aligns with the SmartBear goal of helping teams deploy with confidence by validating the quality of their UI in much less time.
Effective use of AI is critical to solve common testing challenges, especially those related to visual testing. Existing methods of visual testing are unreliable, time consuming, and cumbersome to maintain, and VisualTest fixes these problems through AI and other engineering innovations, significantly improving developer and tester productivity.
Compatible with Cypress, Selenium Python, and Selenium Java, VisualTest is easy to implement and use making it a great choice if you are just getting started. When you run a script, VisualTest automatically takes a screenshot, compares it to the previous version, and highlights crucial visual changes.
VisualTest is the latest in the company’s powerful and cost-effective suite of tools that give development teams unprecedented confidence in their software releases.
The Latest
While 87% of manufacturing leaders and technical specialists report that ROI from their AIOps initiatives has met or exceeded expectations, only 37% say they are fully prepared to operationalize AI at scale, according to The Future of IT Operations in the AI Era, a report from Riverbed ...
Many organizations rely on cloud-first architectures to aggregate, analyze, and act on their operational data ... However, not all environments are conducive to cloud-first architectures ... There are limitations to cloud-first architectures that render them ineffective in mission-critical situations where responsiveness, cost control, and data sovereignty are non-negotiable; these limitations include ...
For years, cybersecurity was built around a simple assumption: protect the physical network and trust everything inside it. That model made sense when employees worked in offices, applications lived in data centers, and devices rarely left the building. Today's reality is fluid: people work from everywhere, applications run across multiple clouds, and AI-driven agents are beginning to act on behalf of users. But while the old perimeter dissolved, a new one quietly emerged ...
For years, infrastructure teams have treated compute as a relatively stable input. Capacity was provisioned, costs were forecasted, and performance expectations were set based on the assumption that identical resources behaved identically. That mental model is starting to break down. AI infrastructure is no longer behaving like static cloud capacity. It is increasingly behaving like a market ...
Resilience can no longer be defined by how quickly an organization recovers from an incident or disruption. The effectiveness of any resilience strategy is dependent on its ability to anticipate change, operate under continuous stress, and adapt confidently amid uncertainty ...
Mobile users are less tolerant of app instability than ever before. According to a new report from Luciq, No Margin for Error: What Mobile Users Expect and What Mobile Leaders Must Deliver in 2026, even minor performance issues now result in immediate abandonment, lost purchases, and long-term brand impact ...
Artificial intelligence (AI) has become the dominant force shaping enterprise data strategies. Boards expect progress. Executives expect returns. And data leaders are under pressure to prove that their organizations are "AI-ready" ...
Agentic AI is a major buzzword for 2026. Many tech companies are making bold promises about this technology, but many aren't grounded in reality, at least not yet. This coming year will likely be shaped by reality checks for IT teams, and progress will only come from a focus on strong foundations and disciplined execution ...
AI systems are still prone to hallucinations and misjudgments ... To build the trust needed for adoption, AI must be paired with human-in-the-loop (HITL) oversight, or checkpoints where humans verify, guide, and decide what actions are taken. The balance between autonomy and accountability is what will allow AI to deliver on its promise without sacrificing human trust ...
More data center leaders are reducing their reliance on utility grids by investing in onsite power for rapidly scaling data centers, according to the Data Center Power Report from Bloom Energy ...