Top Visual Regression Testing Tools

  In high-frequency trading environments, visual clarity is a must-have. One digital-first investment platform with over two million users learnt it the hard way when its UI quality was becoming more difficult to maintain as the team scaled. Each weekly release introduced new components, layout variations, and design tweaks. But without a visual testing layer in place, small regressions often slipped into production. Charts would shift. Buttons would overlap. Colors would break under dark mode. These inconsistencies started affecting investor confidence and increasing customer support volumes. To solve the problem, the platform adopted TestGrid’s AI-powered visual testing capabilities, integrated directly into their existing automation workflows. This allowed them to systematically detect, analyze, and resolve visual bugs early in the SDLC. The Challenge: Visual Bugs Slipping Through Standard Automation The platform’s QA process was already mature in terms of functional covera...

Legacy infrastructure can feel like a ball and chain for even the most established organizations. For one top-tier bank, outdated on-premise systems were reducing efficiency, inflating costs, and limiting scalability. However, by partnering with TestGrid and adopting a hybrid infrastructure model, they turned things around, achieving a 60% reduction in infrastructure spend and unlocking Day-0 OS support. Here’s how they did it and what you can learn from their journey. The Problem: Legacy Systems Holding Back Progress The bank relied on a legacy on-premise infrastructure provider that couldn’t meet modern demands. They faced several critical challenges: The lack of immediate operating system updates and Mobile Device Management (MDM) support left them vulnerable and unable to roll out new features quickly. Without virtual USB and biometric authentication capabilities, the bank struggled to implement secure, user-friendly authentication methods. Their infrastructure provid...

If you’ve been in software long enough, you’ll agree that test infrastructure is under constant pressure. Bloated test suites, rising compute costs, and slow feedback loops hinder development, wasting time, energy, and resources. But pushing on the same levers—more machines, parallelism, and caching—will only get you so far. There’s a need to switch things up. Fortunately, a fresh perspective comes from the AI frontier, more particularly from DeepSeek. Founded in 2023, the Chinese AI startup rethought efficiency for its R1 model. Instead of scaling compute endlessly, it optimized resource use and prioritized workload-aware scheduling, ultimately leading to performance gains at a fraction of the cost. If streamlining test infrastructure is high on your priority list this year, this blog post delves into DeepSeek’s principle on efficiency, highlighting the lessons you can learn and apply to achieve rapid, reliable test cycles. What DeepSeek’s Architecture Teaches Us About System Design D...

Test automation has historically delivered meaningful gains in software delivery speed and consistency, including an   average ROI of 301% over three years . But those benefits typically come at a cost: upfront investment in custom frameworks and specialized engineers. For lean and fast-moving organizations, insufficient time for thorough testing and high workload also pose a roadblock in their automation endeavours. In 2025, only 1% of teams have entirely replaced manual testing, and 14% still report no automation impact. So, even though the potential is real, the barrier to entry remains high. That’s where the introduction of accessible AI models into test tooling is changing the equation. The innovative technology offers a new set of capabilities, some immediately useful, others still maturing, which require thoughtful integration. This post explores how AI is lowering the barrier to test automation, making it more collaborative and scalable across teams. The Historical Bar...