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Unless they use a low-code test recorder like Mabl or Rainforest (which come with their problems), outsourced QA services all license off-the-shelf development tools to write, run, and maintain test suites. And if you’ve ever tried to do a QA engineer’s job using common IDEs, SCMs, and build servers, you know they’re not optimized for QA.
All QA engineers are handicapped by their tools in two ways:
The broken QA toolchain is the root cause of E2E coverage gaps for software teams everywhere. When we identified that issue, we knew eliminating these gaps would require building the QA Wolf platform from scratch—combining all those fragmented tools into a unified system. Fixing the toolchain meant developing a proprietary tech stack to manage thousands of tests daily, enabling rapid execution and minimal delays. This innovation allows us to deliver quicker testing cycles and broader coverage, setting a standard that no other QA provider can match.
Let’s take a step back and look at…
On the surface, you might think that a QA engineer needs only an IDE for writing test code, an SCM for version control, a build server to run tests, a messaging app, and access to the application logs. Not much different than a product engineer. But that only scratches the surface:
At the very minimum, a QA engineer also needs…
You can buy some QA tools off the shelf and set others up in-house, but each solves only a tiny part of the workflow. The lack of integration between the tools creates friction, slowing down everyday tasks like filing bug reports or managing test failures. The time lost switching between tools adds up quickly, especially when running hundreds of tests daily while reviewing failures, clearing flakes, and reporting bugs.
And if you are among the few who decide to unify the toolchain, you’ll discover that new challenges emerge. Simply connecting the dots isn’t enough—you need a system that actively reduces noise, prioritizes meaningful insights, and evolves with your testing needs.
You’re undoubtedly aware of the challenges QA engineers face but may not realize that the root cause of their issues is, more often than not, the inadequate tech stacks they’re forced to work with. These problems are widespread because developers outnumber QA engineers, so tools aren’t customized for testing workflows. Here are some specific symptoms of this widespread problem:
Creating tests takes longer
QA engineers create and maintain tests locally to reduce lengthy debugging on remote servers. However, they must perform a final check on the remote server, which forces them to switch context out of the IDE and their local execution environment because IDEs don’t automatically integrate with the remote build environment for E2E test execution, nor do they support line level execution for E2E tests. And if that final check fails, QA engineers can either fix the remote build—which means repeating the run of the entire suite remotely until the test is fixed there—or risk leaving the failure unaddressed and assume it’s a false positive. The trouble with false positives is that they create noise, which obscures actual bugs.
Executing tests takes longer
Due to limited infrastructure, QA engineers often can’t fully parallelize their test suite. While they could add an outsourced grid, like GitHub Actions, to their complicated toolchain, the costs are often untenable. Ultimately, most development teams limit parallelization with sharding or sequential runs.
Debugging tests takes longer
Test executors can’t save the state of the test at failure points, and IDE test frameworks don’t support selective line execution in tests. That means every time a QA engineer attempts to fix a buggy test, they have to restart the entire thing from the beginning.
Reporting bugs takes longer
QA engineers always struggle to access comprehensive logs and video histories of test failures. Instead, they must use a cumbersome process of juggling multiple tools:
This inefficiency hampers both debugging and bug reporting.
Without a mechanism for automatic retries, tools to track flake rates over time, or data visualizations to identify root causes, QA engineers can’t reliably prevent or resolve flakes. As a result, those flakes create false positives, and we saw above what happens with those.
Without time-tracking systems that are integrated with the IDEs and runners, QA managers have no way to understand the specific breakdown of time QA engineers spend creating tests, running tests, resolving flakes, etc. This lack of detailed insights makes it challenging for leadership to manage resources and effectively plan team capacity in real time.
Without the right tools for identifying and communicating real-time priorities, engineering departments struggle to focus on the most urgent tasks. This lack of coordination among different squads and stakeholders means that important issues, like newly discovered bugs, are overlooked in the short term. As a result, teams risk missing their release deadlines because their QA resources are not notified of priority shifts in a timely manner.
In short, there is no incentive.
In-house teams don’t fix the broken QA toolchain because it is complex and resource-intensive and is not their priority. They focus on shipping features, not integrating fragmented tools like test execution, bug tracking, and time management. Building a unified system requires expertise, time, and ongoing maintenance, which teams can’t justify.
Like in-house teams, vendors adopt the same tools and workarounds mentioned above—juggling multiple tools, relying on manual processes, etc.—which inevitably slows them down and limits the quality of their service. But it gets worse; most outsourced QA firms charge hourly rates, so they have no incentive to build custom tools at their own cost to increase their efficiency. On the contrary, inefficiencies—like time spent switching between tools or debugging flaky tests—directly benefit the vendor by increasing billable hours. This lack of accountability leaves their customers footing the bill for slower testing cycles, incomplete coverage, and suboptimal results. Without a system built specifically for the realities of QA, vendors perpetuate the same inefficiencies their customers hired them to solve, ultimately undermining their value as a service.
Those familiar with Playwright or Cypress might be thinking, “The runtime environment already gives us most of these features.” And, yes, these tools are excellent for running and debugging tests. But, they don’t integrate with the infrastructure in a way that supports advanced test execution features. Additionally, they can’t give you the comprehensive project management metrics QA teams require, such as overall utilization or utilization per task type.
But if you couple the runtime environment with infrastructure and project management systems, features like arbitrary line execution, automatic flake retries, and real-time prioritization suddenly become possible. Furthermore, integrated metrics dashboards can collect and display data on execution times, pass/fail rates, flake trends, and resource usage, which give QA managers the insights they need to plan capacity and refocus resources to improve overall team performance.
We had to build this system from the ground up because piecing together various off-the-shelf products simply didn’t work. To offer high-speed, high-volume testing with 80% coverage in four months, zero flakes, executed fully in parallel with all maintenance included, we needed a vertically integrated platform that supports every aspect of the QA workflow—from execution to reporting to continuous improvement.
Test creation, maintenance, execution, and measurement unite into a set of QA tools hosted in the cloud. This removes the hassle of managing separate tools for testing, debugging, and reporting. Furthermore, it lets QA engineers execute and troubleshoot multiple tests simultaneously in a single session. Specific features enhance our QA engineer’s abilities:
AI-enhanced test creation and maintenance
QA Wolf uses AI to generate context-aware suggestions for test creation and maintenance. We train our AI the same way we onboard our QA engineers. QA engineers review every AI-created test or suggested fix to make sure they meet our exacting standards.
Selector suggestions
The platform’s AI-driven suggestions reduce the time our QA engineers spend fiddling with CSS selectors or XPath syntax, and that speeds up test creation.
Built-in debugging and rollback tools
Version history and rollback features allow QA engineers to revert to a previous state with a single click. An easy-to-access list of video results and logs provides context on previous test runs, helping them pinpoint problems and implement fixes.
Bug reporting
Our push-button bug reporter automatically includes console logs, videos, and intelligently generated reproduction steps. QA engineers simply review and submit the generated report using integrations with our customers’ collaboration tools.
Selected line execution
Our line execution feature enables targeted debugging. QA engineers can run arbitrarily selected test sections with a single click without restarting the entire suite or test. This gives QA granular control over execution and isolates problematic areas for review and adjustment.
Containerized environments
QA Wolf supports unlimited parallel test runs using a sophisticated infrastructure design that runs each test in a separate container. Teams don’t contend for available test execution nodes. QA engineers can execute suites without delays, no matter how many tests there are, as many times as needed, free of charge.
Intelligent retries
Our system uses AI to intelligently rerun failed tests during\ in a given QA cycle and attempts to fix failures in place during that same cycle. It can delay re-running a test for a few minutes to allow time for servers to stabilize, or it can interact directly with the web application to adjust or remove problematic test fixtures, ensuring a smoother test run on the next attempt. Additionally, it can execute code more slowly within a test to determine if the issue lies within the test code itself rather than the web application.
Single execution environment
Our QA engineers don’t need to run locally before running on the build server. Tests are developed and maintained using a single environment with a single configuration, which prevents “works-on-my-local-box” syndrome.
Workflows optimized for QA engineer accountability
Task Wolf gives our team real-time visibility into customer requests, bug reports, and test maintenance needs so we can hold ourselves accountable. It centralizes data from multiple sources, creating a single system of record for all QA activities.
Real-time prioritization
Much like triage in a hospital emergency room, our system has configurable rules that can dynamically bump task priority based on other characteristics, such as how long the task has been waiting or who requested it. QA engineers take tasks from the top of the queue, and as tasks gain importance, they jump closer to the top.
Flake dashboard
Our system includes a comprehensive dashboard that allows testers to visualize the entire QA cycle, including flake retries. This helps them identify and resolve bottlenecks.
QA Wolf solved E2E automation
Every QA manager secretly knows that the tools available to QA engineers outright suck for testing. They weren’t selected for the dynamic, iterative, and precision-driven work that QA engineers perform day-to-day. Instead, they are hand-me-downs from product engineering, DevOps, or project management teams. Consequently, QA engineers make do, creating workarounds for their tools’ shortcomings that slow their team down and limit their effectiveness.
QA Wolf took a fundamentally different approach. Our model, where we charge by the test instead of by the hour, ensures our success is tied directly to delivering efficient, low-maintenance tests that reduce costs, eliminate inefficiencies, and increase test reliability. To be aligned with our customers’ needs and priorities, we invested in custom-built technology that optimizes all of the QA team’s workflows, from engineers to their senior leaders. The insights from the boots-on-the-ground help our customers accelerate release schedules, improve software quality, and free teams to focus on innovation. With the right tools and approach, QA doesn’t have to be a bottleneck—it can be a competitive advantage.
QA Wolf is a hybrid platform & service that helps software teams ship better software faster by taking QA completely off their plate.
© 2024 QA Wolf, Inc.
