Deep Barot is the Founder and CEO of ContextQA, the only AI testing platform that understands context. He brings decades of experience across DevOps, full-stack engineering, cloud systems, and large-scale platform development. Before starting ContextQA, he led engineering initiatives in fintech, healthcare, IoT, and enterprise software, building automation frameworks, CI/CD pipelines, cloud deployments, and mission-critical systems for companies like Credit Acceptance, GE, Guardhat, and Perficient. His work has always focused on solving real engineering bottlenecks through automation and scalable architectures. At ContextQA, he applies that expertise to eliminate flaky tests, accelerate releases, and help teams achieve reliable, predictable quality with an AI-powered no-code, low-code, and pro-code testing platform. Deep believes AI should empower engineers and make software delivery faster, stable, and trust-driven.
Testing Fundamentals
types of software testing

These Are The Types of Software Testing QAs Actually Need to Know

If you’re a QA, you’ve probably heard of a million different types of software testing. More than you could ever want to. There’s unit testing, integration testing, system testing, acceptance testing, performance testing, security testing… the list goes on and on (and on). It’s enough to make your head spin. And with so many different […]

Uncategorized Test Automation
Visual Regression Testing for UI Stability

Visual Regression Testing for UI Stability: The 2025 Benchmark Guide

TL;DR: Visual regression testing catches layout shifts, CSS cascade errors, and cross-browser rendering divergence that functional automation cannot detect. The biggest operational challenge is false positive noise from pixel-perfect comparison. Teams that solve this with AI-assisted comparison and intelligent thresholds run visual testing as a sustainable CI practice, not a one-sprint experiment they abandon. This […]

AI in Testing
Self-Healing Test Automation Tools

Self-Healing Test Automation Tools: What the Data Shows in 2026

TL;DR: Self-healing test automation tools use AI to repair broken test locators when UI changes, eliminating the maintenance overhead that consumes 30 to 40 percent of QA engineering time according to Capgemini’s World Quality Report. They work reliably for the locator fragility category. They do not fix state isolation bugs, environment failures, or broken test […]