AI in QA: Shifting from manual to autonomous testing

Sizing up manual vs. autonomous testing, we can’t help but admit that manual testing offers detailed control over test scenarios. Yet, experienced tech pros know that manual testing is labor-intensive and time-consuming. This is not to mention human errors.

Moreover, in intricate systems, subtle interdependencies can lead to regression bugs that manual tests often miss. The cognitive load on testers to anticipate every possible user interaction becomes overwhelming, particularly with the rise of microservices and real-time data processing.

Artificial Intelligence makes transitioning to autonomous testing closer than ever before. Let’s face it, Machine Learning algorithms can comprehend and embrace much more information, far beyond human capability.

They dynamically generate test cases, predict potential failure points, and mimic user behavior so naturally that it’s just something else. With next-gen testing tools, QA teams can scale their efforts in line with accelerated development cycles.

This article provides a detailed overview of the benefits, challenges, and best practices for embracing this transformation.

Understanding the shift from manual to autonomous testing

The beginning: Manual testing

An initial approach relied on human testers: they executed test cases, identified defects, and validated functionalities. On the one hand, this approach implies a deep understanding of the user experience (at the same time, it hugely depends on the testers’ level), but it also has several challenges.

• Need for human effort: Same tasks day to day blur eyes so that accuracy decreases over time. This intensive labor demand can strain resources, especially if you deploy several times per day.

• Slower feedback loops: Manual testing is often the last step before the release, and if there are a lot of bugs, it slows down the overall time-to-market.

• Scaling is difficult: Scaling manual testing efforts is impractical, if we are talking about modern sophisticated apps. Adding more testers doesn’t linearly increase efficiency due to coordination overhead (you will need more managers as well) and variability in tester expertise.

• Hidden setbacks: As we said above, this approach hinges too much on the testers’ level. Inexperienced ones might overlook critical issues like concurrency problems or security vulnerabilities. Another issue is inconsistency, which makes maintaining test documentation cumbersome.

The transitional move: Automation in QA

Every limitation in some sense contributes to the progress. This is how automated testing tools appeared. Inventors wanted to increase efficiency with predefined test scripts but without human intervention. It is much easier; however, still requires significant human effort:

• Script creation: Test engineers must write detailed scripts for each test scenario. And now imagine you have a comprehensive financial app with many features, so your engineers must go through every possible user path. Time-consuming, innit?

• Maintenance: Test scripts become outdated with each new build within the app. Maintaining and updating these scripts is a continuous challenge that can consume as much time as creating them.

• Analysis: Automated tests generate vast amounts of data. Interpreting results and diagnosing failures still rely heavily on human judgment.

This is why we now see many AI-native testing startups springing up among relatively traditional automation. For example, Mabl or Distributional, which were established by former Google and Intel engineers.

Modern approach: Autonomous testing

Namely, AI in quality assurance. It takes automation a step further since the AI systems can:

• Learn from the application: AI algorithms analyze code changes, user flows, and historical defect patterns to generate and modify test cases dynamically.

• Adapt on their own: The application changes so do the system and the testing strategy.

• Optimize the process: The machine learning model conducts a risk assessment and focuses on areas most likely to contain defects.

• Be more accurate: AI-driven testing tools simulate user behavior and explore edge cases that humans might not even consider. This way, you dramatically increase coverage, sometimes up to 95%.

ℹ️ OwlityAI doesn’t offer software testing services only. It offers free time, focus, and your future growth. Self-heal tests and adapting to application changes without manual intervention enable a higher level of your efficiency, test accuracy, and business scalability.

The benefits of autonomous testing in QA

1. Increased efficiency and speed

Think of the road you take to the supermarket. In 90% of cases, it is the same road, sometimes long and inconvenient because you have to go around other buildings and offices. And now imagine that the road has been paved directly, and instead of 10 minutes you spend 2. This is what transitioning to autonomous testing does:

• Test creation: For complex applications, the traditionally manual process can take approximately 1-2 hours per test case. AI generates thousands of test cases in minutes.

• Test execution: Manual testers will execute an average test case suit within several days. AI/ML model executes tests in parallel and will complete the same suit up to 90% faster. A regression suite that takes 40 hours manually can be completed in 4 hours.

• Analysis and reporting: Automatical analysis of test results, pattern identification, and defect prioritization — these AI testing features reduce analysis time by up to 80%.

2. More accurate, fewer human errors

One of the most obvious benefits of AI in QA is risk mitigation. We are all humans, and we all make mistakes sometimes, especially when it comes to repetitive and complex tasks. However, as they say, there is an app (AI) for that.

• Consistency: Next-gen tools perform the same steps over and over again. Without steps away from the plan, there is just no room for mistakes.

• Complex scenarios aren’t an issue: AI algorithms manage intricate test scenarios with multiple variables.

• Defect detection: Machine Learning models, when trained properly, identify anomalies that people can’t even imagine, so the detection rate soars.

If automated testing can replace at least 50% of testing efforts compared to manual methods, just imagine the AI influence.

3. Scalability and flexibility

New approach scales drastically easier and handles complex apps while adapting to changes 24/7/365.

• Scalability: AI-powered tools can execute thousands of test cases across multiple platforms and environments simultaneously, something impractical with manual testing.

• Adaptability: They adjust automatically to application changes. When UI elements or APIs are modified, self-healing updates test scripts on their own.

Conditional example:

A SaaS startup experiencing rapid growth needed to scale its QA efforts. The SaaS model implies relatively frequent updates and releases. This is where autonomous software testing comes into play:

> They are now able to drastically increase test coverage without spending money on hiring, teaching, and mentoring.

> App updates, AI adjusts, and tests run automatically — maintenance efforts can be reduced by 70%.

4. Continuous learning and improvement

Modern testing tools learn non-stop with each test execution. So do with user interactions, enhancing their accuracy and effectiveness over time. What does it mean in practice?

• Machine learning: Models identify found defects, analyze them, and categorize them to retrieve information in similar future cases. Also, they track anonymized user behavior to predict where the most significant “pressure” will appear, and where issues may arise. Such an approach helps the model to prioritize test areas.

• Behavior analysis: Every model is trained on particular data sets. Sometimes, these sets are irrelevant in real life. Tracking real ongoing user interactions, the tool adds new knowledge to previous pre-trained data, refining test cases.

• Performance enhancement: Continuous learning leads to smarter testing strategies, identifying performance bottlenecks and security vulnerabilities proactively.

5. Cost savings

Next-level efficiency and a smaller human team obviously lead to substantial cost savings.

• On regular payments: Decreased reliance on manual testing allows teams to reallocate resources or operate with smaller teams.

• On maintenance: Self-healing tests reduce the time spent on updating and fixing test scripts.

• On decision-making and resource allocation: With more time and money, you can invest them in strategic initiatives and new ways of profit generation.

Clear-cut calculation:

We have an enterprise software company. By implementing AI in quality assurance, they reduced annual QA expenditure by 35%. In very rough calculations, it equals USD 350,000, considering the average QA Engineer salary in the US is over USD 80,000 a year.

6. Enhanced compliance and security testing

No need to repeat how important security is. Over the past year, over 1 billion customer records have been stolen, and the number is rising every day. Autonomous testing improves compliance and security validation.

• Regulatory compliance: AI tools can automatically generate and execute tests that ensure applications meet industry regulations.

• Security vulnerability detection: Models also can identify potential security threats by analyzing patterns and behaviors that might indicate vulnerabilities.

Very beneficial features for strictly-regulated industries like fintech or the medical industry. HIPAA, AML and KYC, PSD2, HITECH — the list is endless. And AI’s got you covered.

Challenges of transitioning to autonomous testing

To stay ahead — keep learning

In fact, adopting new technology implies upfront investment in time, resources, and even willpower effort. New AI-driven tools aren’t an exception. Your teams will need to integrate the mentioned tools into existing workflows and adapt their testing strategies.

When growing a tree, you need to water it, ensure fertilization, and pest protection. The same story with your QA team — give them tools, learning materials, and time to embrace new approach.

However, this investment is not limited to adopting new technology. For executives and entrepreneurs, it’s about future-proofing the company. The learning curve might take time and grind, however, the long-term benefits far outweigh the initial hurdles.

Where to get quality data

High-quality data is the foundation, the base, the salt. Feeding the model with deliberately shoddy data is the first nail in the coffin of your project. Based on poor or insufficient data the model will make inaccurate predictions and unreliable test cases.

Proven sources to train ML models

1. Previous test results: Previously created test cases (including those created by humans), test results, and defect logs are invaluable source to learn from previous successes and failures.

2. User-related data: The way users interact with the software helps AI understand real-world usage patterns and identify typical and atypical user flows.

3. Source code repositories: Access to the codebase and version history allows AI to analyze code complexity, dependencies, and recent changes that may affect testing priorities. Definitely a risky source, but only if your CISO was responsible for National Public Data in April 2024, where almost 3 billion user records were stolen, including highly sensitive information.

4. Production logs and monitoring data: Logs from live environments offer real-time insights into system performance, errors, and unusual behaviors that require testing attention.

5. Third parties: DataRobot, Figure Eight, and other similar providers offer curated datasets for specific industries and applications.

Note: data sources must be accurate, up-to-date, and comprehensive. This is the only way AI models can effectively support autonomous testing initiatives.

Manual vs. autonomous testing: How to strike the balance

Autonomous testing delivers. That’s true. Yet, this is rather a complementary than a substitution element. Human testers bring critical thinking, creativity (where to be honest, AI falls short of the mark), and irreplaceable intuition. Exploratory testing, usability assessments, and testing for unexpected user behaviors are areas where manual testing still outperforms AI.

On the other hand, over-reliance on AI without adequate human oversight can lead to epic failures. Take Zillow’s case. Online real estate marketplace developed an AI-driven home-buying algorithm called iBuying. They launched a program called Zillow Offers which handed over the right to purchase homes for flipping to an AI model.

And it overestimated property values, leading to the company buying houses at inflated prices. Without sufficient human review and market analysis, Zillow accumulated unsellable inventory, resulting in a loss of over USD 500 million and the eventual shutdown of the program after only 5 days from the start.

So, a hybrid approach to software testing is the cornerstone that multiplies AI and human testers’ efforts. AI excels at handling repetitive, data-intensive tasks, while human testers are better suited for scenarios requiring empathy.

Best practices for implementing autonomous testing

1. Pilot project

What sets a smart cookie out of a crowd? Before they dive, they dip their toes. In other words, they always start with a pilot project to assess the effectiveness of new tools without overwhelming their team or resources. Choose a small, well-defined project where it’s completely clear what to do and how to measure success.

Top three nuances C-suite execs must be aware of during pilots:

1/ Clarity: Objectives and KPIs

• Define success from the outset: You should clearly understand what you want to achieve within the pilot. There is no room for double-talks, so use SMART or a similar framework for goal setting: to reduce testing time by 30% by the end of the year with the same team on board by implementing AI.

• Outcomes must be measurable: The more fog — the less impressive the result. Choose quantifiable metrics and make sure to link them to business goals.

2/ Set up a dedicated budget

• Mix the teams: Assemble a dedicated team with both QA professionals and developers familiar with AI tools.

• Budget wisely: Don’t be a penny pincher and ensure a sufficient budget for the tool and training. Rest assured it’ll pay off.

3/ Manage expectations

• Keep stakeholders posted: All relevant parties must be informed about the pilot’s scope, objectives, and potential impact.

• Embrace the iterative approach: The first pancake is always lumpy. Be ready to iterate based on initial findings.

2. Blend your approaches

Follow the following steps to seamlessly integrate your current workflow with a novelty.

> Evaluation first

• Assess the current state of things: Document your existing QA processes to identify integration points.

• Identify tool compatibility: Check if the AI tool supports integrations with your current project management (Jira, Asana, etc.) and CI/CD pipelines (Jenkins, GitLab, etc.).

> Use API and plugins

• The tool’s API should connect with your existing systems. For example, use OwlityAI’s API to fetch test results directly into your dashboard.

• If available, install plugins that facilitate integration: Visual Studio Code or Eclipse for IDEs, for example.

> Move on step by step

• Don’t be too ambitious: An easy pace ensures a robust way. Begin with regression or unit test automation. Further is more.

• Monitor and adjust: Regularly check the integration’s performance and make adjustments before a full-scale rollout.

A bit more hands-on tips

• Make AI tests fully automated: Tune your new tool to trigger the test cycle whenever new code is pushed to your repository.

• Customize reporting: Create a convenient format for reports. Ensure they match your previous reporting practice.

• Collaborate with IT: Your IT department is supposed to address any technical challenges during integration. Help them to help you — provide all the necessary information and documentation.

3. Control and track to keep a health system

Autonomous testing is not a "set it and forget it" solution. At the moment we have not stepped into the ultimate AI era. Modern technology, whether it’s Artificial Intelligence or Quantum Computing, still requires human oversight to remain effective and aligned with objectives.

Keep an eye on metrics

• Performance indicators: Track test execution time, defect detection rate, and false positives/negatives. These are the minimal set of metrics that just must be “stalked” all the time.

• Model accuracy: Regularly assess the AI model’s predictions against actual outcomes.

Listen to feedback, prolong its collection

• Human oversight: QA engineers should review AI-generated test cases and results to provide feedback.

• Iterative refinement: Use the feedback to retrain or adjust the AI model for improved performance.

Update data sources

• Refresh training data: Ensure your AI model is trained on the most recent data to adapt to new patterns or code changes.

• Leverage user feedback: Include insights from customer support or user analytics to identify new areas for testing.

4. Train and support

Your team is your bread and butter, to say the least. So ensure your team has the necessary skills for the successful adoption of autonomous testing:

• Vendor-supported training: Inquire about training programs offered by the AI tool provider. OwlityAI, for example, offers webinars and hands-on workshops with our experts.

• Customized learning paths: Create training modules tailored to your team’s specific needs and skill levels.

💡Top tip: Develop internal expertise

Create AI champions: Identify team members who are already digging into the AI topic and provide them with advanced training to become internal experts.

Encourage them to share their POVs with the rest of the team through regular meetings or company-wide chats — support channels where team members can ask questions and share solutions.

The future of QA with autonomous testing

This technology will evolve further and maybe even more than we expect. As Sam Altman said on GenAI, we have seen only 1/5 of its real capabilities. So just imagine what AI testing tools will be able to do in the next 5 years.

And there is a question. Are you ready to fall behind without these features in your testing cycle?

• Flawless predictive analytics: For now, some seasoned human testers can predict even better than current AI models leveraging their intuition and experience. They need more time, though. However, in the coming years, AI will catch up and overplay.

• Next-level Natural Language Processing (NLP): Further NLP development will enable AI tools to better understand requirement documents, user stories, and especially customer feedback. Therefore, we should expect more accurate test case generation and more effective validation of user experiences.

• Integration of AI with IoT and Edge Computing: As the Internet of Things (IoT) expands, AI-driven testing will extend to a wider array of devices and platforms. Autonomous testing tools will need to adapt to test not just software applications but also the complex interactions between interconnected devices.

• AI environment: QA Engineers and testers will figure out how to get the most out of the AI in quality assurance. And vice versa, future tools will learn from human insights and provide intelligent suggestions. AI becomes more intelligent, testers become more productive. Win-win.

What to take into account

> Stay competitive longer: AI-empowered companies can innovate rapidly and respond to market changes with agility. This adaptability is a key differentiator between successful champions and those who “just gave it a shot”

> Save money for innovations: Initial investment may seem significant. Yet, the long-term cost savings from reduced manual labor and optimized resource utilization are overweight, hands down.

> Mitigate risks: Predictive analytics and comprehensive testing reduce the risk of critical failures, security breaches, and compliance issues. We don’t want you to become a new TechCrunch security rubric hero.

Why choose OwlityAI

Looking to embrace autonomous testing? OwlityAI stands out. Here’s why:

Advanced features:

• Self-healing automation: App changes, OwlityAI adapts immediately. No need for continuous manual efforts, no need for daunting maintenance.

• Intelligent test generation: You provide access to your code repository, the tool analyzes it, and generates test cases based on it and user behavior patterns.

• Predictive analytics: Identifies high-risk areas in your application and allows you to shift your focus to strategic market-matter issues.

Ease of integration

• Seamless workflow integration: Our tool integrates effortlessly with popular development tools and CI/CD pipelines like Jenkins, GitLab, and Jira.

• Open APIs: Provides APIs and plugins for customization and integration with your existing systems.

• User-friendly interface: Designed with an intuitive interface that reduces the learning curve and accelerates adoption.

• Calculator: Find out how much you will save with OwlityAI.

Continuous support and training

• Dedicated customer support: Offers expert assistance to ensure you get the most out of the platform.

• Comprehensive training programs: Provides comprehensive blog on autonomous software testing and workshops to empower your team with the necessary knowledge.

Security and compliance

• No room for a breach: Ensures that your data is protected with enterprise-grade security protocols.

• Regulatory compliance: Helps maintain compliance with industry standards.

Bottom line

Transitioning to autonomous testing is an inevitable step. Companies that recognize and adapt to this shift will reap substantial benefits. And now, it’s still early-age time to embrace tools like OwlityAI. Equip your team with AI in Quality Assurance and support them with appropriate knowledge to excel in an increasingly complex market.

Rest assured, AI continues to evolve, and so does OwlityAI. Our mission is to make our testing processes remain cutting-edge and effective. Make the strategic move towards autonomous testing with OwlityAI and lead your company to the next-level Quality Assurance.