AI in Software Testing: How Artificial Intelligence Changes QA
AI in QA is becoming so dynamic that even weekly updates can’t keep up with the pace.
A year ago, we discussed ChatGPT’s impact on test automation — it was a novelty. Now, entire businesses specialize in AI for QA and software testing using artificial intelligence. It is backed by serious players, ambitious innovations, and, naturally, some very public failures.
In this article, we’ll talk about
- The current state of AI software testing;
- Proven benefits of AI in software testing—and where the hype might mislead you
- Real experiences and case studies on AI in QA testing
- Practical tips to reduce risks and future-proof your QA strategy with AI-powered QA tools
Also, heads up: we recently partnered with Virtuoso AI, a top-tier provider of AI-driven tools for automation. We’ve integrated their solutions into our projects, from planning through automated regression testing.
We’ll share that experience, plus insights into other tools we believe have real AI under the hood.
Key Takeaways: Implementing AI in Software Testing
#1. Traditional testing’s days are numbered. AI QA testing increases coverage by 85% while cutting costs by 30%.
#2. Real metrics matter. AI testing delivers 80% faster test creation, 40% better edge case coverage, and 90% reduction in bug reporting time.
#3. ROI varies by company size. Startups see almost immediate returns from AI testing services; enterprises need 3-4 testing cycles for positive ROI.
#4. AI enhances humans. Successful implementations use AI for repetitive tasks, freeing testers for complex scenarios.
#5. Self-healing eliminates maintenance. AI automatically adapts to UI changes, solving automation’s biggest headache.
#6. Security gaps persist. 47% of AI testing users have no cybersecurity practices in place.
#7. Beware of fake AI. Many tools rebrand basic automation as “AI” without true machine learning benefits.
#8. AI has limitations. Current technology struggles with creativity and requires quality training data.
#9. Bias creates blind spots. Noname AI-powered testing tools can inherit biases from training data, creating uneven test coverage.
We use AI QA to cut testing costs by over 30% while increasing coverage.
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Current State of AI in QA 2025
Numbers are forgettable and often boring without context.
But they help you to see the trends, especially when they come from reliable sources. When ISTBQ, Gartner, or British Department for Science, Innovation and Technology (DSIT) cover the impact and the future of software testing with AI — you take notice.
So we give just a few numbers summarized from few research results and surveys to help you realize one thing — traditional software development and software testing industry in particular are living their last years.
Industry insights and statistics on AI technologies
- AI-driven testing can increase test coverage by up to 85%;
- Organizations using AI-driven testing reported 30% reduction in testing costs and 25% increase in testing efficiency;
- The global AI market is forecast to reach $1.81 trillion by 2030 from $196.63 billion.
- 47% of current AI users had no specific cyber security practices in place for AI (not everything is so shiny, right?).
Measurable Impacts of AI Systems on Testing Workflows
The real value of artificial intelligence in software testing emerges when we examine concrete metrics and workflow transformations. While AI tools aren’t mandatory for testing, our experience suggests they’re quickly becoming essential for teams seeking competitive advantage in delivery speed and software quality.
Creative consoles + AI case
Bug triage optimization
- Challenge. Multiple software testers — QA team members — were logging duplicate issues, creating significant inefficiencies in bug assignment and prioritization.
- AI Solution. Implemented DeepTriage, an AI-powered triage system that automatically categorizes, detects duplicates, and assigns bugs based on historical patterns.
- Measurable impact. 80% decrease in analysis time and bug report creation, allowing the team to process large numbers of test cases without proportional increases in triage time (like in traditional software testing).
Comprehensive test coverage enhancement
- Challenge. Documentation constraints meant test creation focused primarily on positive scenarios, leaving gaps in edge case testing.
- AI solution. Deployed generative AI (ChatGPT) to analyze requirements and automatically generate comprehensive test scenarios, including negative cases and boundary conditions.
- Measurable impact. 80% faster test case creation while achieving a 40% increase in edge case coverage. The AI identified test scenarios human testers had consistently overlooked.
Automated bug report generation
- Challenge. Converting unstructured customer feedback into standardized bug reports was time-consuming and inconsistent.
- AI solution. Leveraged natural language processing to transform customer comments into properly formatted, detailed bug reports with severity classifications.
- Measurable impact. 90% reduction in report processing time with improved communication clarity, allowing developers to begin fixes without clarification cycles.
Requirements testing acceleration
- Challenge: User stories and requirements lacked consistency and structure, complicating test creation.
- AI solution. Applied a combination of AI tools (including ChatGPT and Grammarly) to analyze, restructure, and standardize software requirements documentation.
- Measurable impact. 500% reduction in requirements testing time, with a 50% increase in quality improvements through automated detection of ambiguities, contradictions, and spelling issues.
Automated test results reporting
- Challenge. Manual integration of test results from multiple sources created reporting bottlenecks during regression testing cycles.
- AI solution. Implemented Microsoft Power BI with AI-driven analytics to automatically collect, correlate, and visualize test results across platforms.
- Measurable impact. 30% improvement in data representation quality while cutting report generation time by 50%, enabling faster decision-making during release cycles.
Our experience in implementing AI in software testing have skyrocketed since then, but it was a great start that allows us to truly believe in benefits of using AI in small and eneterprise-level projects.
Enterprise vs. Startup implementation metrics
We’ve since implemented AI in testing services across organizations of different sizes, revealing important differences in adoption patterns and ROI timelines:
Enterprise implementation metrics
- Initial setup time for AI testing tools: 2-3 weeks (vs. 4-8 weeks for traditional frameworks)
- ROI timeline: Positive returns typically seen within 3-4 testing cycles
- Test coverage improvement: Average 32% increase in test scenario coverage
- Regression testing time reduction: 45-60% decrease in execution time
- Bug detection improvement: 28% more defects identified pre-release
Startup implementation metrics
- Initial setup time: 3-5 days (vs. 2-3 weeks for traditional frameworks)
- ROI timeline: Positive returns often immediately visible in first testing cycle
- Test coverage improvement: Average 50% increase in test scenario coverage
- Regression testing time reduction: 60-75% decrease in execution time
- Bug detection improvement: 35% more defects identified pre-release
Key performance indicators for AI testing success
Through multiple implementations, we’ve identified the KPIs that best measure the impact of AI on testing workflows.
| KPI | What to Measure | Why It Matters | Practical Gains |
| Test Creation Efficiency | – Number of test cases created per hour- Pre- vs post-AI metrics | Speeds up coverageReduces tester burnout | – Faster ramp-up for new features- More time for exploratory testing |
| Coverage Metrics | – Percentage of requirements covered- Types of scenarios included | Ensures full alignment with business needsReduces risk of missed defects | – Clearer test scope- Balanced focus on both positive and edge cases |
| Defect Detection Rate | – Quantity of bugs found- Severity and potential impact | Measures the effectiveness of QAHighlights areas needing extra attention | – Fewer production incidents- Higher user satisfaction |
| Time-to-Feedback | – Time from code commit to actionable test results- Bottlenecks in test pipelines | Keeps dev cycles shortReduces rework caused by late bug discovery | – Faster releases- Lower overall development costs |
| Maintenance Overhead | – Time spent updating or fixing test scripts- Frequency of script failures | Indicates if AI self-healing or adaptive scripts are working effectively | – Reduced manual updates- More stable regression suites |
| False Positives/Negatives | – Proportion of test runs reporting incorrect failures or overlooked defects | Affects trust in AI-driven testsImpacts QA efficiency and response times | – Higher tester confidence- Less time wasted on investigating “ghost” issues |
| AI Adoption ROI | – Cost savings from automation vs. investment- Break-even point in test cycles | Proves the business value of AIHelps justify further AI QA budget | – Clear financial benefits- Strong case for scaling AI solutions |
Our experience implementing AI in software testing has accelerated dramatically since these initial projects. What’s become clear is that the benefits apply equally to small projects and enterprise-level testing efforts, with tailored approaches yielding significant efficiency gains across the testing lifecycle.
Startups see ROI bump in their first testing cycle. Enterprises in 3-4 cycles.
Use the power of AI software testing to boost your product’s competitiveness.
Technical Applications of AI in Software Testing
Even the best manual testers are limited by time and scope. AI is changing that. With machine learning and predictive analytics, AI enhances traditional manual testing processes. From test planning to execution, AI-driven tools bring precision and efficiency, making manual testing smarter and more effective.
Importantly, AI doesn’t eliminate the need for human testers; it helps them work more efficiently and focus on complex issues.
Test planning and design
Test case generation allows analyzing historical data and user stories to generate comprehensive test cases. AI is used to increase the overall coverage of the testing process (yes, large number of tests doesn’t necessarily mean quality, but we still rely on human intelligence to filter trash out).
Risk-based testing relies on machine learning algorithms to prioritize test cases based on potential risk and impact.
Defect prediction is based on using AI and ML predictive models to identify areas of the application most likely to contain defects.
Test execution and management
Test data management will be easier with automating the creation and maintenance of test data sets using AI-driven tools.
Test environment optimization uses AI systems to manage and optimize test environments, ensuring they are representative of production.
Visual Testing is all about employing AI-powered visual validation tools (like Vision AI) to detect UI anomalies that human testers might miss.
Collaboration and reporting
AI-powered reporting allows generation of detailed and actionable test reports with insights and recommendations using natural language processing.
Collaboration tools cover integrating AI with collaborative tools to streamline communication between testers, developers, and other stakeholders.
And now, to the most exciting part. End-to-end automated testing done right with AI-based test automation tools. It’s a mouthful, but it is exactly what you need to be thinking about it 2024.
TestFort Client Success Stories
Artificial Intelligence in Software Test Automation
Integrating AI into software testing helps get the most from automation testing frameworks. Right now, there is hardly an automated test scenario that cannot be somehow enhanced with tools for AI QA.
Self-healing scripts
Self-healing scripts use AI algorithms to automatically detect and adapt to changes in the application under test, reducing the need for manual script maintenance.
Dynamic element handling allows AI to recognize UI elements even if their attributes change, ensuring tests continue to run smoothly. As UI testing becomes essential to any minor and major launch, AI can assist immensely.
Intelligent test case prioritization
Risk-based prioritization relies on AI to analyze code changes, recent defects, and user behavior to dynamically prioritize test cases.
Optimized testing ensures critical paths are tested first, improving overall test efficiency.
AI-driven regression testing
Automated selection uses AI tools to automatically select relevant regression test cases based on code changes and historical test results.
Efficient execution speeds up the regression testing process, allowing for faster feedback and quicker releases.
Continuous integration and continuous delivery (CI/CD)
Automated code analysis employs AI tools to perform static and dynamic code analysis, identifying potential issues early in the development cycle.
AI-powered deployment verification involves using AI to verify deployments by automatically executing relevant test cases and analyzing results.
Performance testing leverages AI to simulate user behavior and load conditions, identifying performance bottlenecks and scalability issues.
AI in test maintenance and evolution
Adaptive test case generation uses AI to continuously generate and evolve test cases based on application usage data and user feedback.
Predictive maintenance applies machine learning to predict and address test script failures before they impact the CI/CD pipeline.
Automated test refactoring utilizes AI to refactor test scripts, ensuring they remain effective and efficient as the application evolves.
Continuous testing
Seamless integration ensures AI in testing software integrates with CI/CD pipelines, enabling continuous testing and faster feedback.
Real-time insights provided by AI offer immediate feedback on testing results, helping teams make informed decisions quickly.
By incorporating AI into automated testing, teams can achieve higher efficiency, better test coverage, and faster time-to-market. AI-driven tools make automated testing smarter, more reliable, and more adaptable to the ever-changing software landscape.
As you can see AI in software testing takes many forms: generative AI for test scripts, natural language processing for, vision and even audio processing, machine learning, data science, etc. These are all mixed. The good news is that testing using artificial intelligence doesn’t require you to have deep understanding of algorythms, and tech, and types of ML learning. You just need to choose the right AI testing tools… and not fall for the lies.
We have the right tools for AI automation testing. Will your product benefit from them? Let’s talk
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Manual Test and Software Test Automation Tools for AI
We’ve been in the market for AI tools for over a year, searching for a partner that truly enhances our automated testing on both front and back ends. Many tools we encountered used AI as a buzzword without offering real value. It was frustrating to see flashy promises without substance.
Then we found Virtuoso AI. It stood out from the rest.
Viruoso AI and other AI-enhanced automation testing tools
After evaluating dozens of testing solutions claiming AI capabilities, we’ve identified tools that deliver genuine value rather than just marketing hype. Virtuoso AI stands out with features that transform testing efficiency while reducing technical debt. Here’s what sets true AI-powered testing tools apart:
Codeless automation. We can set up tests just by describing what they need to do. No coding necessary, which means quicker setup and easier changes.
Functional UI and end-to-end testing. It covers everything from button clicks to complete user flows. This ensures your app works well in real-world scenarios, not just in theory.
AI and ML integration. The power of AI is in it’s ability to learn from your tests. It gets smarter over time, improving test accuracy and reducing manual adjustments.
Cross-browser testing and API integration. With this tool we can test how your app works across different web browsers and integrates API checks. This means thorough testing in diverse environments – a must for consistent user experience.
Other AI tools for testing
Besides Virtuoso AI, here are a few other notable artificial intelligence software testing tools available on the market:
- Applitools. Specializes in visual AI testing, offering tools for automated visual validation and visual UI testing.
- Testim. Uses machine learning to speed up the creation, execution, and maintenance of automated tests.
- Mabl. Provides an AI-driven testing platform that integrates with CI/CD pipelines, focusing on end-to-end testing.
- Functionize. Combines natural language processing and machine learning to create and maintain test cases with minimal human intervention.
- Sealights: Focuses on quality analytics and continuous testing, offering insights into test coverage and potential risk areas.
Benefits of AI tools for manual testing
Manual testers still handle creative reasoning and user insight best. However, AI automates the repetitive chores that eat up their time. Below is a quick rundown of the benefits AI brings to manual testing.
| Benefit | Why It Matters | Practical Gains |
| Faster Analysis | AI sifts through logs, detects duplicate bugs, and highlights key data. | Frees testers for complex tasks and reduces admin work. |
| Smarter Prioritization | Algorithms rank test tasks by impact and risk, not guesswork. | Focus on top-risk areas first, avoiding time spent on low-value tasks. |
| Adaptive Learning | Tools “learn” from previous bugs and failures to suggest likely trouble spots. | Identifies overlooked edge cases, improving coverage and quality. |
| Rapid Reporting | Raw feedback converts into structured bug reports in seconds. | Minimizes manual input errors and speeds up the dev team’s workflow. |
| Deeper Test Coverage | AI-based test generation uncovers hidden or underestimated scenarios. | Helps testers find corner cases, catching defects earlier. |
| Streamlined Collaboration | Built-in analytics and dashboards keep QA, dev, and stakeholders aligned. | Fewer back-and-forth clarifications and more effective teamwork. |
| Reduced Maintenance | Self-healing scripts adapt to minor app changes without manual fixes. | Less rework on test scripts, especially during frequent releases. |
Use these tools to handle the repetitive grunt work of testing and monitoring. Manual testers then have more time for critical analysis, spotting usability issues, and ensuring a better user experience overall.
What to look for when choosing testing AI systems
When evaluating these tools and testing activities they cover, remember to check their true AI capabilities, scalability, integration, and support systems to ensure they meet your needs.
But let’s not ignore the broader market. There are many AI tools available, each with its own strengths and weaknesses. Here’s what to consider when evaluating them:
- True AI capabilities. Look beyond the buzzwords. Ensure the tool offers genuine AI-driven features, not just automated scripts rebranded as AI.
- Scalability. Can the tool handle large-scale projects? It should adapt to your growing needs without performance issues.
- Integration. Check how well the tool integrates with your existing systems and workflows. Seamless integration is crucial for efficiency.
- Support and сommunity. A strong support system and an active user community can make a significant difference. Look for tools with responsive support teams and extensive documentation.
Choosing the right AI tool for testing is critical. It’s easy to get caught up in marketing hype. Stay focused on what truly matters: real, impactful features that improve your testing process. Our experience with Virtuoso has been positive, but it’s essential to do your own research and find the best fit for your needs.
In summary, while AI tools can optimize testing, be cautious and discerning. Not all tools deliver on their promises. Seek out those that offer genuine innovation and practical benefits.
Experience all the benefits of using AI for software testing. Let us choose the best testing tools for your product!
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Implementation Reality of Artificial Intelligence QA Strategy
let’s examine real-world applications where AI transforms testing outcomes. These case studies demonstrate practical implementation approaches and measurable results.
How to optimize testing with generative AI
A mid-sized eCommerce platform struggled with maintaining test coverage across multiple microservices (inventory, shipping, billing). Their QA team spent hours writing manual test scenarios whenever a service changed.
Challenges
- Frequent updates caused outdated test scenarios.
- Manual test generation was time-consuming and prone to oversight.
- Insufficient coverage for edge cases involving complex service interactions.
What we did
Generative AI test creation
- We fed the platform’s user stories and API endpoints into a generative AI model (like ChatGPT).
- The model produced dozens of new test scenarios, including edge cases that manual testers often missed.
AI-driven prioritization
We used an AI-based tool to rank which microservice flows were at highest risk, focusing on those for immediate test creation.
Outcomes
- Reduced manual scenario-writing time by nearly a third.
- AI-suggested edge cases led to a 25% increase in test coverage across critical microservices.
- Test leads could quickly edit or refine scenarios in collaboration with the model’s suggestions.
Generative AI can augment a QA team by rapidly creating relevant test scenarios, freeing testers to concentrate on exploratory testing and deeper integrations.
Automation testing with powerful and secure AI-based software
A growing fintech company was scaling its web app, which handles sensitive user data. They needed faster releases, reliable cross-browser tests, and robust security to protect customer information.
Challenges
- Frequent UI updates. Each time the user interface changed, manual testing and old automated scripts had to be rebuilt or fixed.
- Complex Data & APIs. Multiple API calls and real-time data made test coverage difficult.
- Strict security requirements. Handling financial transactions demanded careful data protection and compliance.
Our approach
We decided to use Virtuoso AI, our partner’s test automation solution that combines AI-driven testing with codeless scripting. This allowed us to cover the entire testing process from end to end while keeping security top of mind.
Why Virtuoso?
- Codeless automation. Tests are created by describing actions rather than writing scripts, so new tests or updates happen faster.
- Self-healing scripts. When the UI changes, Virtuoso automatically updates the affected test steps, cutting down script maintenance.
- Enhanced security. Virtuoso supports data masking and on-premises configurations, helping protect sensitive financial information.
- Cross-browser & API integration. It tests the front end on different browsers and can also verify backend API calls—all in one place.
Implementation & integration
Test design & setup
- First, we mapped the app’s main user flows and API endpoints.
- Using Virtuoso’s codeless interface, our QA team built automated tests that included data-driven checks and edge cases.
Self-healing UI tests
- Whenever the fintech’s UI changed (e.g., new buttons or layout tweaks), Virtuoso’s AI pinpointed the updates and adjusted the relevant test steps.
- We no longer had to rewrite scripts for every minor change, saving time and cutting costs.
Security & compliance
- We used data masking features to replace sensitive user information with dummy values.
- Virtuoso’s on-prem and secure cloud options made sure all tests met the startup’s financial compliance needs.
Cross-browser checks & API validation
- Virtuoso ran simultaneous tests on Chrome, Firefox, and Edge to ensure a consistent user experience.
- Its integrated API testing flagged issues in the backend early, reducing production bugs.
Results
- Faster release cycles. Automated tests were ready and updated more quickly, allowing the fintech to ship new features on schedule.
- Reduced maintenance effort. Self-healing scripts eliminated 60% of the usual manual fixes after UI changes.
- Stronger data protection. Masked test data maintained security without limiting coverage.
- Improved test coverage. With both UI and API tests under one platform, we caught more bugs before release.
By partnering with Virtuoso AI, our team provided a powerful, secure automation solution that scaled with the fintech’s growth. The codeless setup and self-healing scripts cut down on repetitive maintenance while preserving tight security controls.
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Risk Assessment: When AI Testing Falls Short
If you feel like the previous part confirms that you may be out of work… soon, don’t sell yourself short, at least for now. Here are the limitations AI has and will have for a considerable amount of time.
- Lacks creativity. AI for software testing algorithms experience big problems generating test cases that consider edge cases or unexpected scenarios. They need help with inconsistencies and corner situations.
- Depends on training data. Don’t forget — artificial intelligence is nothing else but an algorithm, a mathematical model being fed data to operate. It is not a force of nature or a subject for natural development. Thus, the quality of test cases generated by AI depends on the quality of the data used to train the algorithms, which can be limited or biased.
- Needs “perfect conditions.” I bet you’ve been there — the project documentation is next to none, use cases are vague and unrealistic, and you just squeeze information out of your client. AI can’t do that. The quality of its work will be exactly as good or bad as the quality of the input and context turned into quantifiable data. Do you receive lots of that at the beginning of your QA projects?
- Has limited understanding of the software. We tend to bestow superpowers on AI and its understanding of the world. In fact, it is truly very limited for now. May not have a deep understanding of the software being tested, which could result in missing important scenarios or defects.
- Requires skilled professionals to operate. For example, integrating a testing strategy with AI-powered CI/CD pipelines can be complex to set up, maintain, and troubleshoot, as it requires advanced technical skills and knowledge. Tried and true methods we use now may, for years, stay much cheaper and easier to maintain.
How AI-Based Software Testing Threatens Users and Your Business
There is a difference between what AI can’t do well and what can go wrong even if it does its job perfectly. Let’s dig into the threats related to testing artificial intelligence can take over.
- Bias in prioritization and lack of transparency. It is increasingly difficult to comprehend how algorithms are making prioritization decisions, which makes it difficult to ensure that tests are being prioritized in an ethical and fair manner. Biases can influence artificial intelligence models/tools in the data used to train them, which could result in skewed test prioritization.
Example. Suppose the training data contains a bias, such as a disproportionate number of test cases from a particular demographic group. In that case, the algorithm may prioritize tests in a way that unfairly favors or disadvantages certain groups.
For example, the training data contains more test cases from men than women. The AI tool may assume that men are the primary users of the software and women are secondary users. This could result in unfair or discriminatory prioritization of tests, which could negatively impact the quality of the software for underrepresented groups.
- Overreliance on artificial intelligence in software testing. Lack of human decision-making reduces creativity in testing approaches, pushes edge cases aside, and, in the end, may cause more harm than good. Lack of human oversight can result in incorrect test results and missed bugs. Increased human oversight may lead to maintenance overheads.
Example. If the team relies solely on AI-powered test automation tools, they may miss important defects that could have significant impacts on the software’s functionality and user experience. The human eye catches inconsistencies using the entire background of using similar solutions.
Artificial intelligence only relies on limited data and mathematical models. The more advanced this tech gets, the more difficult it is to check the results’ validity, and the riskier is overreliance. This overreliance can lead to a false sense of security and result in software releases with unanticipated defects and issues.
- Data security-related risks. Test data often contains sensitive personal, confidential, and proprietary information. Using AI for test data management may increase the risk of data breaches or privacy violations.
Example. Amazon changed the rules it’s coders and testers should follow when using AI-generated prompts because of the alleged data security breach. It is stipulated that ChatGPT has responded in a way suggesting it had access to internal Amazon data and shared it with users worldwide upon request.
Wrapping up: Generative AI and Machine Learning in QA
AI for QA testing isn’t a passing trend. We’ve seen teams speed up test creation by 80%, cut bug triage time in half, and push updates faster. This matters because software quality isn’t a “nice to have.” It’s a vital part of staying competitive.
AI does the heavy lifting, humans bring the insight
- AI tools handle repetitive tasks, deep-dive into logs, and spot patterns faster than most people can.
- Skilled testers add creativity, judgment, and the ability to handle tricky edge cases.
Real results, real caution
- Pay attention to security and data quality. Garbage in, garbage out still applies for AI in testing.
- “AI-driven” doesn’t always mean real AI. Check for genuine machine learning capabilities.
- Bias is a risk. Without oversight, AI can overlook key user groups or misclassify bugs.
Next steps
- Start with a clear plan. Decide which areas of your QA process need AI the most.
- Measure everything — test efficiency, coverage, and defect rates.
- Tweak and improve. AI shines in iterative environments.
We help companies way beyond the tasks like “generate test scripts the right way” or “build test automation framework integrating AI solutions”. Our team knows how to combine AI-driven speed with human know-how to deliver quick, reliable, and meaningful results. It is a systematic approach — auditing your QA, analyzing which tools can help save money, speed up, and improve quality, and building a practical roadmap.
If you want to see what AI can do for your QA, let’s talk.
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