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Quality Engineering
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Posted on
May 7, 2025

The evolution of QA: from manual testing to AI-powered automation tools

The evolution of QA: from manual testing to AI-powered automation tools
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Quality Assurance (QA) has evolved dramatically over the years. 

From the early days of manual testing, where human ingenuity and attention to detail drove the process, to the current era of AI-powered automation tools, the journey of QA reflects the broader advancements in software development practices. 

In this blog, The Evolution of QA: From Manual Testing to AI-Powered Automation Tools, we explore the fascinating journey of Quality Assurance, highlighting the transformative shift from traditional manual testing to the cutting-edge adoption of AI-driven automation tools. 

Readers will gain valuable insights into the key milestones of QA evolution, learn best practices for integrating modern tools into their workflows, and discover actionable strategies for QA professionals to stay ahead in this dynamic field. Whether you're new to QA or a seasoned expert, this blog will provide you with the knowledge and inspiration to embrace the future of testing..

The evolution of QA

The early days: manual testing as the backbone of QA

Manual testing laid the foundation for modern QA practices. It involves testers manually executing test cases without the aid of automation tools, ensuring the software behaves as expected. 

The key strengths of manual testing include:

  • Human insight: Manual testers excel at understanding user behaviour and identifying edge cases.
  • Flexibility: Testers can adapt to changes in requirements and focus on usability and user experience.
  • Exploratory testing: Testers can investigate unforeseen scenarios, uncovering issues that automated scripts might miss.

However, as software complexity grew, the limitations of manual testing, such as scalability, time consumption, and susceptibility to human error, became apparent.

The advent of automation: a game changer

To address these challenges, automation tools like Selenium and QTP emerged. These tools enabled QA teams to execute repetitive test cases more efficiently and with greater accuracy. Key benefits included:

  • Speed: Automation dramatically reduced the time required for regression testing.
  • Consistency: Automated scripts ensure consistent execution of test cases, reducing human error.
  • Scalability: Large-scale testing became feasible with automation.

Despite these advantages, traditional automation has its own challenges. Test script maintenance, the need for programming expertise, and limited adaptability to dynamic UI changes often hindered its effectiveness.

The AI revolution in QA: Ushering in a new era

AI has propelled QA into a new era of innovation. AI-powered tools leverage machine learning and predictive analytics to enhance testing processes. Here's how AI is transforming QA:

  1. Predictive analytics: AI analyses historical test data to identify high-risk areas, enabling testers to focus their efforts effectively.
  2. Automated test generation: Tools like Test.ai generate test cases by analysing user behaviour, ensuring better coverage.
  3. Visual testing: AI tools such as Applitools use advanced algorithms to detect visual discrepancies across different platforms and resolutions.
  4. Anomaly detection: AI-powered platforms like Mabl identify anomalies and suggest fixes in real time, improving defect detection.
  5. Self-healing tests: AI tools like Testim and Functioning can detect and adapt to changes in the application, minimising the need for manual script updates.

Leading AI-Powered QA Tools

Here are some AI-powered tools that are reshaping QA practices, with details on their features, benefits, and accessibility:

UI and functional Testing
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  1. Testim
    • Features: Self-healing tests, AI-driven insights for test creation, and maintenance optimisation.
    • Impact: Accelerates test execution and reduces maintenance efforts.
    • Website: Testim.io
    • Type of testing: UI and functional testing.
  2. Functionize
    • Features: Natural language scripting, AI-driven execution, and self-healing capabilities.
    • Impact: Reduces reliance on coding expertise for automation.
    • Website: Functionize.com
    • Type of testing: Functional and performance testing.
  3. SmartBear
    • Features: Tools like TestComplete and AI-driven script creation, API testing, and performance monitoring.
    • Impact: Provides an all-in-one solution for testing various application types.
    • Website: SmartBear
    • Type of testing: functional testing, API testing, load testing, UI testing.
  4. Applitools
    • Features: Visual AI for automated UI testing across multiple platforms, ensuring consistent user experiences.
    • Impact: Enhances visual testing accuracy, outperforming manual inspections.
    • Website: Applitools.com 
    • Type of testing: Visual and cross-browser testing.
  5. Mabl
    • Features: Low-code automation, AI-driven anomaly detection, and advanced reporting.
    • Impact: Simplifies adoption for non-technical testers and improves test reliability.
    • Website: Mabl.com 
    • Type of testing: Functional and regression testing.
  6. Katalon Studio
    • Features: Unified testing platform for web, API, mobile, and desktop applications with AI-powered analytics.
    • Impact: Seamlessly integrates manual and automated testing workflows.
    • Website: Katalon.com
    • Type of testing: web, API, mobile, and desktop testing.
  7. Ranorex
    • Features: AI-enhanced GUI testing with drag-and-drop test creation and robust reporting.
    • Impact: Lowers the barrier for non-programmers to adopt automation.
    • Website: Ranorex
    • Type of testing: Functional Testing, Regression Testing, Cross-Browser Testing, UI Testing

Visual and Cross-Browser Testing
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  1. Applitools
    • Features: Visual AI for automated UI testing across multiple platforms, ensuring consistent user experiences.
    • Impact: Enhances visual testing accuracy, outperforming manual inspections.
    • Website: Applitools.com
    • Type of testing: Visual and cross-browser testing.
  2. Sauce Labs
    • Features: AI-powered debugging and testing across multiple environments in the cloud.
    • Impact: Ensures comprehensive testing coverage for diverse platforms.
    • Website: SauceLabs.com
    • Type of testing: Cross-browser and performance testing.
  3. Perfecto
    • Features: AI-powered, cloud-based mobile and web testing with insights and real-time debugging.
    • Impact: Enhances mobile app testing with comprehensive device coverage.
    • Website: Perfecto
    • Type of testing: Mobile Testing, Web Testing, Cross-Browser Testing, Cloud Testing.
  4. Percy (Cypress using BrowserStack AI features)
    • Features: Visual testing integrated with Cypress and BrowserStack.
    • Impact: Detects visual changes across multiple environments.
    • Website: Percy
    • Type of testing: Visual regression testing.

Accessibility testing
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  1. Axe devtools
    • Features: Accessibility testing powered by AI.
    • Impact: Ensures compliance with accessibility standards like WCAG.
    • Website: Axe devtools
    • Type of testing: Accessibility testing.

Regression and risk-based testing
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  1. Mabl
    • Features: Low-code automation, AI-driven anomaly detection, and advanced reporting.
    • Impact: Simplifies adoption for non-technical testers and improves test reliability.
    • Website: Mabl.com 
    • Type of testing: Functional and regression testing.
  2. Appsurify
    • Features: AI-driven testing prioritisation based on code changes and risk analysis.
    • Impact: Focuses manual QA efforts on high-risk areas to improve efficiency.
    • Website: Appsurify 
    • Type of testing: Risk-Based Testing, Regression Testing, Test Optimisation.
  1. TestCraft
    • Features: Codeless Selenium-based testing with AI-driven optimisation.
    • Impact: Speeds up test creation and maintenance.
    • Website: TestCraft.io
    • Type of testing: Web application testing.
  2. TestRigor
    • Features: AI-powered functional testing using plain English instructions.
    • Impact: Reduces reliance on coding skills for automation.
    • Website: TestRigor.com
    • Type of testing: End-to-end functional testing.

Mobile App Testing
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  1. Test.ai
    • Features: AI-driven app testing that mimics user interactions and validates user-centric scenarios.
    • Impact: Expands exploratory testing capabilities through automation.
    • Website: Test.ai
    • Type of testing: Mobile app and user behaviour testing.
  2. Perfecto
    • Features: AI-powered, cloud-based mobile and web testing with insights and real-time debugging.
    • Impact: Enhances mobile app testing with comprehensive device coverage.
    • Website: Perfecto
    • Type of testing: Mobile Testing, Web Testing, Cross-Browser Testing, Cloud Testing.

API and data-driven testing
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  1. Mockaroo
    • Features: Generates mock data for testing purposes.
    • Impact: Provides realistic test data to simulate real-world scenarios.
    • Website: Mockaroo.com
    • Type of testing: Data-driven testing.

  2. SmartBear
    • Features: Tools like TestComplete and AI-driven script creation, API testing, and performance monitoring.
    • Impact: Provides an all-in-one solution for testing various application types.
    • Website: SmartBear
    • Type of testing: functional testing, API testing, load testing, UI testing.

  3. Kusho AI
    • Features: AI-enhanced exploratory testing and defect prediction.
    • Impact: Helps identify high-risk areas and enhance test coverage.
    • Website: Kusho.ai
    • Type of testing: Exploratory, API testing and predictive testing.

Exploratory Testing
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  1. Kusho AI
    • Features: AI-enhanced exploratory testing and defect prediction.
    • Impact: Helps identify high-risk areas and enhance test coverage.
    • Website: Kusho.ai
    • Type of testing: Exploratory, API testing and predictive testing.

Test Case Documentation and Generation
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  1. Gen AI (e.g., Chatgpt/Gemini/Prompting)
    • Features: Assists in generating test cases, reviewing scripts, and creating documentation through conversational AI.
    • Impact: Enhances productivity by providing quick, intelligent suggestions.
    • Website: OpenAI.com https://gemini.google.com/app 
    • Type of testing: Test case generation and documentation.
  2. Test case studio
    • Features: Simplifies manual test case documentation and export.
    • Impact: Saves time for testers by automating case generation.
    • Website: Test Case Studio
    • Type of Testing: Manual test case management.
  3. Scribe (AI Documentation)
    • Features: Automatically documents workflows and test procedures.
    • Impact: Simplifies process documentation and onboarding.
    • Website: ScribeHow.com
    • Type of testing: Process documentation and training.

Comparing Manual Testing with AI-Powered Automation
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Aspect Manual testing AI-Powered automation Real-time examples Metrics
Speed Time-intensive Rapid execution of repetitive tasks AI-powered tools like Testim execute thousands of test cases in minutes. Automation reduces test execution time by up to 85%.
Scalability Limited to available resources Scales effortlessly with cloud solutions Sauce Labs enables parallel testing on multiple devices simultaneously. Cloud-based testing platforms improve scalability by 300% for enterprise QA teams.
Adaptability Excels in dynamic and ambiguous scenarios Relies on predefined algorithms Manual testers adapt to evolving game requirements, unlike rigid AI scripts. Manual testing remains crucial for 90% of exploratory testing scenarios
Maintenance High effort for regression testing Self-healing reduces the maintenance burden Testim’s self-healing adapts to UI changes without manual script updates. Self-healing reduces script maintenance time by 70%.
Edge case detection Depends on the tester's expertise AI generates edge cases systematically Test.ai mimics user behaviours to uncover hidden bugs in mobile apps. AI tools improve edge case detection efficiency by 60%.
Cost efficiency Higher in long-term, repetitive projects Cost-effective for large-scale testing Mabl automates regression testing, saving time and resources for enterprise apps. Enterprises report cost savings of 50% with automated regression testing.
User experience testing Superior due to human intuition Limited to scripted or learned behaviour Manual testers excel in usability testing for complex user workflows. Human-led UX testing ensures a 20% higher customer satisfaction rate in product feedback.
Learning curve Minimal training required Requires learning AI tools and scripting Teams need to invest time in mastering Applitools for advanced visual testing. Training for AI tools can take 2–4 weeks, depending on team size and complexity.
Exploratory testing Unmatched ability to explore unknown scenarios Limited exploratory testing capabilities Manual testers discover UX issues during ad-hoc testing of a healthcare app. Exploratory testing accounts for 30–50% of all bugs found during manual testing phases.
Environment coverage Requires physical access to multiple devices Supports testing across various environments Perfecto’s cloud platform provides access to hundreds of device configurations. Cloud-based testing increases environment coverage by up to 95%.
Accuracy Prone to human error AI ensures consistent execution Applitools flags subtle UI mismatches that manual testers might overlook. Visual AI increases defect detection accuracy by 40% compared to manual inspections.
Data analysis Limited ability to analyse large test data sets AI provides actionable insights from analytics Katalon’s analytics dashboard helps prioritise high-risk test areas effectively. Analytics-powered testing can improve bug resolution times by 30–50%.

The future of QA: A balanced approach

The coexistence of manual and AI-powered testing will define the future of QA. While AI tools excel in efficiency and accuracy, manual testing remains irreplaceable for:

  • Human-centric scenarios: Validating the user experience and intuitive usability.
  • Exploratory testing: Tackling scenarios that AI might not anticipate.
  • Dynamic environments: Adapting to rapidly changing requirements.

The ideal QA strategy will blend AI's capabilities with the unique strengths of human testers, creating a comprehensive framework for ensuring software quality.

Conclusion

The evolution of quality assurance from manual testing to AI-powered automation signals a defining shift in how software is built, validated, and released. This shift is not just about saving time or reducing costs, it’s a response to the complexity of today’s systems and the demand for speed without compromising quality. 

AI is now embedded across QA workflows, from test case generation and visual regression to self-healing automation and predictive analytics. Tools like Applitools, Testim, and Functionize are reshaping how test engineers operate, automating tasks once considered too nuanced for machines.

Globally, the market reflects this momentum. The AI testing industry grew from $414.7 million in 2022 to $856.7 million in 2024, with forecasts projecting a rise to $2.75 billion by 2030. North America currently leads adoption, with over 30% market share, followed by Asia-Pacific—particularly India, South Korea, and Japan, as innovation hubs. In the U.S., tech giants like IBM, Microsoft, and Google have embedded AI into their QA stacks, while startups such as Spur are building autonomous testing agents that flag bugs in real time. 

Even niche industries are moving fast: Razer recently launched a QA Copilot to cut game testing time by half, and enterprises are integrating AI across CI/CD pipelines to maintain speed and reliability.

Beyond tooling, the role of QA professionals is also changing. A recent survey shows that 68% of organisations are using or piloting generative AI in testing. QA engineers are transitioning into quality strategists curating AI outputs, training models on test data, and focusing on ethics, test reliability, and edge cases. AI isn’t replacing the QA role, it’s reshaping it. The blend of machine intelligence with human judgment is what will drive quality engineering forward.

As the scope widens, so do the possibilities. Multimodal AI models are already interpreting UI mockups, translating user stories into test cases, and even flagging accessibility issues before launch. 

In the near future, we’ll likely see convergence with quantum testing, blockchain-backed test integrity, and fully autonomous test agents embedded in production. The message from the industry is clear: AI in QA isn’t optional, it’s the new standard for delivering high-performing, reliable software at scale.

Happy Testing!
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Written by
Dhruvi Shah
QA Engineer
Editor
Ananya Rakhecha
Tech Advocate