This article has been contributed to AI Journal by Vitali Shchur, a QA Engineer and invited expert, who brings his hands-on industry experience and perspective on how artificial intelligence, automation, and modern testing practices are transforming software quality assurance. His insights reflect both practical application and forward-looking trends shaping the field.
***
The Evolving Role of Quality Assurance (QA) in Software Development
Quality Assurance (QA) has evolved far beyond manual regression testing. Continually adapting due to the demands of digital transformation, continuous integration and deployment (CI/CD), and modern user expectations. QA serves as a frontline function in today’s environment where software needs to be fast, secure, and functional. Manual testing is being phased out in favor of more intelligent approaches like automation, AI-driven testing, and even chaos engineering, as our software needs to withstand the pressures of real world usage.
Rather than seeking bugs at the end of a development cycle, the focus of QA in the modern world is to ensure adaptive resilience and allocate automation intelligently across the software delivery pipeline. In this article, we outline the impacts of AI, automation, and new technologies in evolving the role of QA from the traditionally reactive, gatekeeping function, to a vital, strategic component of software engineering.
QA Tech: The Evolving Boundaries of QA
Before exploring the effects of technology on QA, let’s clarify these pivotal concepts:
AI-Powered Testing: Test case generation, maintenance, and enhancement through machine learning and computer vision. Examples are self-healing scripts, visual testing, and intelligent prioritization of tests.
Automation Frameworks: Tools such as Selenium, Playwright, or Google’s EarlGrey that enable automation of script based testing on a software feature, thus providing a structure to automation testing.
Chaos Engineering: The practice of intentionally inducing system failures, such as server crashes or network delays, to evaluate the software’s stress resilience. It was popularized by Netflix through tools like Chaos Monkey.
Observability: The monitoring, logging, and tracing of systems for real-time detection of anomalies and their root causes with QA insights streaming directly to DevOps.
These concepts form the foundation for the next generation of QA—intelligent, resilient, and seamlessly integrated within development pipelines. As teams seek to enhance speed and accuracy while reducing manual effort, one area leading this evolution is AI-powered testing.
AI-Powered Testing: Infusing Intelligence into QA
Traditional test automation is resilient—scripts break due to minor user interface (UI) alterations and maintaining test coverage becomes difficult while drawing analysis from results becomes manual work. All these processes are tedious and AI could solve all these problems mentioned above.
With the use of AI, tools like Testim.io, Mabl, and Applitools Eyes are using ML to check expected vs. actual results visually, issuing detection for UI regressions, and suggesting through user behavior data based on tests. Some tools like Functionize even go as far as using NLP to enable QA engineers to draft tests in simple English, which is translated into executable scripts.
For example, Google’s EarlGrey framework for UI testing on iOS performs automated testing in a more stable, less flaky way by syncing test actions to the relevant app states. AI enhancements to EarlGrey make it possible for tests to adapt to changes in layout or loading gaps, which significantly reduces maintenance work.
Thus, through AI integration, QA teams shift from reactive scripting to intelligent, proactive adaptive test authoring in response to real-time product modifications.
Foundational Automation Frameworks for Scalable Quality Assurance
While attention shifts to AI, effective automation frameworks for quality assurance have proven their value as vital cornerstones for any modern organization. Automation tools for browser testing such as Selenium, Cypress, and Playwright along with Appium for mobile automation are tried and tested.
These are used by enterprise customers like Google that utilize EarlGrey and Espresso for asynchronous, deterministic UI testing on iOS and Android respectively. They ensure tests are executed only when the application is stationary, eliminating many false negatives and making CI/CD processes more reliable.
In DevOps pipelines automation frameworks are tightly coupled with version control systems; executing tests at every pull request and deployment. This leads to earlier detection of bugs – shifting left in the development lifecycle – and minimizing expensive rollbacks later during production.
The best organizations go as far as to design their products with automation in mind. Such designs include the use of test IDs, an API-first approach, and modular frameworks that enhance test coverage.
Focusing on Systems Failures: Designing with Intent
While automation ensures consistency in expected scenarios, real-world systems rarely operate under perfect conditions. In production environments, however, systems encounter relentless failures such as server crashes, outages, and degraded services. Achieving these robust conditions proactively is what chaos engineering does.
With the invention of “Chaos Monkey,” Netflix was the first to practice this approach. The tool randomly terminates production instances to test how gracefully the system recovers. This has since evolved into Gremlin, Chaos Toolkit, and several other open-source frameworks that simulate real-world failures like DNS outages, CPU spikes, and latency.
Netflix is now reported to inject thousands of failures daily. Their QA and SRE teams work together to uncover gaps and design robust fallback systems by closing vulnerabilities exposed during the controlled failures.
This evolution reflects a broader shift in QA: from merely validating software functionality to ensuring overall system survivability. Especially in distributed and microservice architectures, this proactive resilience testing is no longer optional—it’s essential.
Use Cases of AI & Automation in QA
With this shift in mindset, leading organizations are embracing AI, automation, and chaos engineering not just as tools, but as strategic pillars of quality. Let’s explore how some of them are putting these innovations into practice.
-
Netflix
Netflix utilizes automation, observability, and chaos engineering simultaneously to maintain an exceptional level of system resilience. Simian Army suite chaos testing is used on production systems, and automated canary analysis performs comparative assessment of metrics during pre-full rollout stages.
-
Facebook (Meta)
Facebook uses Sapienz, an AI-powered tool that employs search-based software engineering to devise test scenarios aimed at reducing app crashes. Over hundreds of pre-production bugs have been automagically resolved by the tool (Meta Research).
These businesses showcase a significant pattern: QA is now integral to each stage of the lifecycle, shifting from being an isolated final step prior to shipping the product.
The Role of QA and Observability: Completing the Feedback Loop
But the evolution doesn’t stop at deployment. In today’s complex, distributed systems, passing test suites isn’t enough. QA must extend into production by embracing observability—gathering logs, traces, and real-time metrics to detect, diagnose, and resolve issues quickly. Platforms like Datadog, New Relic, and OpenTelemetry offer solutions that help engineering teams draw correlations between test outcomes and production metrics.
As an illustration, if a test case passes within the staging environment but latency issues arise in production, observability data assists QA teams in determining the root causes, whether it’s a memory leak, failing API call, or user behavior edge cases. Synthetic testing, in which scripts are executed from global locations, also enables teams to simulate user actions to identify regressions proactively.
Best Practices: Cultivating Tomorrow’s QA Teams
To remain competitive, QA team leaders ought to:
- Acquire AI-driven systems that learn UI and code adaptations to minimize manual test adjustment requirements.
- Integrate automation throughout the software development lifecycle.; don’t restrict it to regression testing.
- Conduct chaos experiments in staging and production environments to discover hidden failure modes.
- Give priority to observability by linking logs and metrics to test outcomes for enhanced pair root cause detection.
- Develop a collaborative proactive team culture and encourage a proactive approach using metrics rather than a reactive stance in isolation.
Companies whose QA processes propel them forward rather than hinder progress experience greater success. A QA system that enhances value accelerates time to market while maintaining trust.
Final Thoughts: QA—Catalyzing Change, Not Halting It
To sum up, the future of QA is agile, intelligent, and integrated. As software complexity grows, QA must evolve from a reactive, checklist-driven discipline into a proactive strategy that assures performance, security, and resilience.
Furthermore, AI and automation free QA engineers from repetitive work, letting them focus on exploratory testing, user journeys, and system reliability. Chaos engineering builds confidence in failure scenarios. And observability ensures fast response when things go wrong.
In a world where user loyalty hinges on performance and reliability, QA is no longer optional—it’s mission-critical.
