Blog

Learn how to run E2E tests faster by writing them for full parallelization. Discover 5 principles for isolated, deterministic tests that eliminate flakes at scale.

Learn what end-to-end (E2E) testing is, why it matters, when to use it, and how to implement it. Explore E2E testing tools, frameworks, common challenges, and best practices for reliable test coverage.

Explore the 12 best AI testing tools in 2026, including Agentic Automated testing, Agentic Manual testing, IDE copilots, and visual regression tools. Compare features, execution models, and tradeoffs to choose the right platform for reliable E2E automation.

Parallel testing runs tests simultaneously to cut execution time from hours to minutes. Discover how it works, why sharding isn’t the same, and how to implement it reliably in CI/CD.

Five cost-effective changes you can make to increase and sustain the velocity of your engineering teams that don't include hiring additional resources. Here's how to do more with less.

Full parallel testing is hard. But in the long run, other approaches to test execution cost more in productivity and dollars. Learn how you can help your team deliver a quality product faster and save money.

Reduce flaky tests with automatic retries, failure pattern tracking, and ownership routing. Build a system that turns instability into signal.

Learn the core principles of software testing, explore best practices for quality assurance, and understand how to apply effective testing strategies for optimal results.

Learn how to write bug reports with expected vs. actual results, repro steps, screenshots/video, and logs—plus severity and environment details for faster fixes.

Smoke testing determines whether a software build is viable for further testing. Learn how smoke tests work, how they differ from sanity testing, and how to run them in CI/CD pipelines.

Functional testing ensures users can complete critical workflows. This guide explains test types, coverage priorities, automation, and common pitfalls.

Device farms reuse hardware to cut costs, but that breaks test stability. QA Wolf built a new system: real devices, clean state, no flakes. Learn how we deliver fast, reliable mobile test automation across Android and iOS.

The Playwright vs. Cypress debate is sure to go on, but when it comes to testing complex workflows, Playwright is better than Cypress for QA Wolf and our customers.

Most AI testing vendors frame “self-healing” as fixing broken selectors. But selectors cause only a minority of failures. See how our AI applies a diagnosis-first approach across six failure categories.

Explore the four types of AI testing tools reshaping how companies build, test, and trust software in production.

Your organization will benefit from accessibility testing in more ways than you are probably aware of. We will tell you how you can build a strategy to reap those benefits.

End-to-end regression testing for generative AI applications is mostly undiscovered territory. We’re blazing the trail, and here are some things we’ve mapped so far.

A no-fluff guide to the top mobile testing frameworks, updated for 2026. Learn when to use Appium, Playwright, Espresso, XCUITest, Detox, and more.

Too many test suites collapse under scale because they lack a clear plan. Discover how to build a test coverage roadmap around workflows that matter, prioritize by impact, and maintain alignment with real product usage as your app grows.

QA Wolf is certified SOC 2 Type II compliant

Codeless AI systems promise speed but remove the determinism and interpretability that make results trustworthy. This post shows how that breaks the experiment—and how code restores it.

Automated testing makes QA faster, but brittleness slows teams with constant rework. AI agents don’t stop tests from breaking—they handle the maintenance, adapting in real time so stability, reliability, and adaptability can reinforce each other.

AI IDEs like Copilot and Cursor generate test code quickly, but they only cover a fraction of QA. Real QA requires planning, investigation, and reporting—the areas that drive release confidence.

Every environment is different — that’s the point — so your end-to-end tests should run in the environments as checkpoints that answer specific contextual questions.

The Test Pyramid was built for a time when infrastructure was slow and parallelism was hard. E2E tests weren’t the problem—execution environments were. That’s no longer true. With the right infra, E2E tests can be fast, stable, and worth the cost.

PR testing sounds fast, but often delays releases with flaky tests, infra strain, and unclear failures. RC testing validates a stable build before release—cutting noise, reducing rework, and aligning QA and dev around quality that scales.

Test automation is easy to start—but hard to sustain. This post breaks down seven common reasons teams stall out after launch, from flaky tests to infrastructure bottlenecks, and how to move from proof-of-concept to real coverage.

PR testing promises speed and confidence—but often delivers flakes, bottlenecks, and burnout. Learn why it goes sideways and what to fix before you make the switch.

You’re not imagining things. Everything that makes E2E regression testing difficult is infinitely more challenging when you’ve integrated with or built on top of Salesforce. 

Mobile tests flake when device, app, or data state isn’t reset. QA Wolf controls all three, so tests start clean and run the same way every time.

The thing about pricing models is that they always seem to make sense, until someone comes around and does something different. In every case, the old pricing model prioritized the vendor’s costs over the buyer’s needs. Which is what’s been happening with QA since the very first QA vendor appeared.

Android emulators are essential for scaling mobile test automation—but instability can derail even the best test suites. This guide covers five proven strategies to reduce crashes, improve performance, and make your end-to-end tests more reliable.

When QA teams own end-to-end testing without the right systems, test maintenance takes over and coverage stalls. Give them the tools to do what they do best: catch meaningful bugs before they reach production.

When it comes to automated end-to-end test suites, the total cost of ownership(TCO) is driven by the amount of testing your team does, the number of tests in a test suite, and how frequently you run them.

Attention, gear heads: Learn the details of our Kubernetes implementation that enables us to execute fully parallelized test runs, whether there are 200 tests in the run or 20,000.

Outsourcing the creation of your automated end-to-end tests and then handing them to your in-house team might seem like a good idea. Here's why your expectations likely don't match reality.

See what we've been up to and what we're planning in Q2 2025!

Crowdsourcing has some valid use cases. But before you decide to go with a crowdsourcing vendor for automated testing, make sure you understand the drawbacks. This blog will tell you when it's is the right testing strategy for your project and when you are better off with something else.

We've come a long way since the Browser Wars and automated cross-browser testing is probably not worth the expense, except in a handful of cases.

Counting tests isn’t the same as understanding your test coverage. These six metrics track signal quality, flake rates, skip rates, and workflow alignment to show whether your coverage helps you catch bugs early—or just slows your team down.

Shifting left doesn’t mean developers should own E2E testing. When they do, test quality drops, coverage gaps grow, and delivery slows. Let devs focus on building—leave black-box testing to QA.

Learn how the AAA framework helps you define your test coverage needs and what you need to validate to feel confident in every build you release.

Modern tools and frameworks make regular web page performance testing easier for teams. We'll provide the code samples to get your team up and running with the performance metrics you need to create a great customer experience.

Discover how random sampling boosts model adaptability, cuts costs, and ensures reliable, up-to-date performance.

Trying to expand test automation before proving value? This post breaks down four common pitfalls that derail early testing efforts—and shows how to shift from stalled initiatives to steady momentum.

QA Wolf and MuukTest may seem similar at first glance, but they serve different needs. QA Wolf’s all-inclusive service for complex projects versus MuukTest’s piecemeal pricing for simpler applications. Dig into the details to determine which service best suits your project’s demands.

Find out how to measure AI session performance with LLMs. We focus on key tasks using weighted scenarios and dynamic metrics, ensuring real-world accuracy and helping you improve performance.

If you’re building your test suite in-house, take a tip from the QA Wolves: Cover the critical workflows across your whole product first and work down to the edge cases—don’t go feature by feature.

Learn why QA Wolf built a custom LLM Orchestration Framework over LangChain or LangGraph, focusing on flexibility, customization, and robust type safety.

In this article we’ll discuss the functional differences between QA Wolf and Testlio™, and the impact they have on achievable test coverage.

We redefine automated test maintenance by using specialized bots for accuracy and efficiency. Here’s how our agents apply that to deliver reliable QA testing.

Golden Datasets fall short in real-world AI projects. Discover how random sampling boosts model adaptability, cuts costs, and ensures reliable, up-to-date performance.

Learn how QA Wolf combines AI with human expertise to automate software testing, generating test code from user workflows and resolving failures. AI handles repetitive tasks, while human oversight ensures accuracy, making testing faster and more reliable.

QA Wolf provides a managed solution for custom, fast-changing software with per-test pricing, while Mabl offers a self-service tool for simpler apps with usage-based pricing. Explore their differences to find the best fit for your QA needs.

Explore 13 notorious software failures, from the Knight Capital failure to the Toyota acceleration problem. Learn how robust end-to-end testing could have prevented these high-impact errors and what we can learn from them.

Integrate DevOps with automated testing to speed up software delivery and improve quality. Learn the best tools and practices today.

Discover the importance of negative testing in software development

Announcing our $36 million Series B and the launch of mobile app testing, which is open for waitlist. The investment is led by Scale Venture Partners with participation from Threshold Ventures and Ventureforgood, and existing investors Inspired Capital and Notation Capital.

See what we've been up to and what we're planning in Q3 2024!

Everyone contributes to quality, but only developers prevent bugs at the source. Learn why prevention starts in the code, and how white-box tests keep your system stable.

See what we've been up to and what we're planning in Q2 2024!

Look before you leap - here's what you should watch out for when considering an AI-powered testing tool.

Learn how to test generative AI applications from the best. Use this curated list to find the information you need.

You learn a lot about the reality of E2E testing when you manage tens of thousands of them each week.

We didn't think charging for test runs or hours worked aligned the price of automated E2E testing to the value you actually receive. Read why we charge by test under management instead and how it creates a fairer, more predictable pricing structure for everyone.

Exactly how long can a test suite survive on its own while developers continue to ship new code? Read on to find out.

As startup operators, we’ve learned that there is often a "story behind the story" that goes untold. In this post, we'll go back in time and cover the winding path that led to our first $1.1 million in funding from Sahil Lavingia, Naval Ravikant, and pre-seed lead Notation Capital.

Our evolution from an open source command line interface to a full-service QA solution actually happened very naturally as we looked deeper and deeper into the problem at the heart of test automation.