A beginner’s guide to adopting autonomous QA solutions

2022 and 2023 were terrible for Intuit, the mother company for TurboTax and QuickBooks. The company faced a lot of criticism for glitches and errors in calculations. Nothing groundbreaking — fintech has always been a niche hard to operate in and easy to fail. 

And now imagine another scenario. Intuit implemented AI-driven quality assurance. Of course, this is only an imaginative case; however, the tax operator could mitigate risks of breaches and fatal mistakes.

Automated regression testing lowered the double defects (bugs slipped to the prod after code changes) and reduced the time required to validate each release. As a result, faster and smoother releases — users are happy, the company rolls in it, and the Director of testing no longer needs a couple of beers every Friday to relieve. 

And the most interesting is that this fictional example has a real back. Katalon’s 2024 State of Software Quality Report points out that 75% of those surveyed already use any sort of automation, and nearly 50% use (or plan to implement) autonomous QA solutions. 

Easier said than done, right? That’s why we created this article — to hash out autonomous QA for beginners. 

What are autonomous QA solutions?

In a nutshell — the next big thing, with Artificial Intelligence, Machine Learning, almost total automation, and wise optimization. Now, recall traditional manual testing. Counts on humans and their intuition too much, innit? Even if we bring up conventional automated testing, it still requires beforehand prepared scripts and additional time to maintain them relevant.

AI-driven quality assurance represents the idea that the machine (predefined and supervised algorithms) independently designs, executes, and evaluates tests. In other words, the system learns from your data and updates its knowledge base in real-time by scanning your application functionality, user behavior, and any other changes.

Key features

• Test case generation: As we said above, AI tools scan and process all the external (user behavior, code commits, etc.) and internal (documentation, bug logs, etc.) information and automatically generate test cases to cover any possible case. This significantly extends coverage, including edge cases. 
• Self-healing: This feature detects and adapts to changes in the application’s user interface or codebase: UI element changes or the developer commits new lines — the system instantly “awakes” and updates the corresponding test scripts. 
• Continuous testing: Continuous Integration/Continuous Deployment (CI/CD) pipelines will contain a new element for continuous testing. Wherever a defect appears, it’s promptly identified and addressed by this element (the tool). 

Benefits

• Time savings: These tools significantly reduce the time required for testing cycles by automating test case generation and execution. You can see a 95% speedup compared to the manual approach. 
• Cost efficiency: The fewer people in a team, the less money you spend. Therefore, you save more. Additionally, early defect detection minimizes the post-release fix expenses — up to 93% cost reduction overall.
• Improved test accuracy: As with any automation, AI-powered testing minimizes human errors and enhances the precision of test execution.

78% of software testers in the US use AI to get those things done with impeccable accuracy. 

Why adopt autonomous QA solutions?

1/ Efficiency:

AI-powered testing tools take over repetitive and time-consuming tasks and save you time. This accelerates the testing cycle and provides development teams with instant feedback. 

2/ Improved quality:

Quite logical: when you detect a broader range of defects and do this earlier in the CDLC, you will ensure a better quality. Moreover, autonomous testing can analyze vast amounts of data to identify patterns and anomalies, as well as predict areas where bugs are more likely to occur. 

3/ Scalability:

With your app evolution, the next-gen testing tool evolves too. It can handle increased testing demands without a proportional increase in resources. Whether you are an enterprise or just a young startup, with such tools, you’ll get consistent quality assurance practices. 

Example

You might find this example worn out a bit; however, there is no other that illustrates the efficiency of AI testing tools better. So, Netflix. 

The streaming giant implemented autonomous testing tools longer before the modern AI madness. From advanced suggestion algorithms to autonomous self-healing, Netflix knows the ropes of modern approaches to creating efficient and flexible, yet predictable workflow. 

Key steps for adopting autonomous QA solutions

Step 1. Assess your QA needs

Before a full-fledged adoption, assess your existing testing workflow. Look for bottlenecks, inefficiencies, long-winded or too insufficient processes. Spot areas where manual testing is time-consuming and error-prone.

Two frameworks for this assessment

1. Test Maturity Model integration (TMMi): A step-by-step approach that clarifies your QA maturity. It helps to benchmark your testing processes against best practices and identify gaps where AI can improve efficiency.
2. Google’s Testing Pyramid: Helps to categorize tests (unit, integration, end-to-end) and assess automation opportunities. Basically, this approach has the same goal (spotting areas for fast and fruitful automation) but different instruments.

Of course, not all QA aspects should be automated immediately. After an assessment, you will have specific areas with the most potential impact and which you are able to measure. Namely:

• Regression testing: Often change code? This might lead to test failures. Autonomous solutions stay up and running in any case — they generate, execute, and maintain regression tests automatically after every change.
• Flaky test detection: UI element changes and you need to create a new test. Yet, autonomous QA tools have self-healing: they dynamically adjust to such modifications and cover all changes with appropriate tests.
• Exploratory testing assistance: Yes, exploratory testing is still mainly a human prerogative. However, AI can complement human exploratory testing — it identifies patterns, anomalies, and edge cases. This helps improve test coverage and defect detection.

Step 2. Research and choose the right tool

The cornerstone here is how well the new testing tool aligns with your team’s skill set, development workflow, and infrastructure. What to consider?

• Ease of use: Too complicated and sophisticated tools are okay, but for old-hand professionals or learning geniuses. For this reason, an intuitive interface and low-code/no-code option are always better.
• Number and relevance of integrations: Good compatibility with your existing CI/CD pipeline and development environment can prevent many issues.
• Scalability: Does the tool support your current needs, is it adaptable to future growth and complexity?

Why OwlityAI

1. Low-code implementation: No previous QA/QE experience is needed. Do you have astute non-tech specialists in the team? With OwlityAI you can involve them at 101% so that they can contribute to the product.
2. Many integrations: Seamlessly connects with popular tools (Jenkins, Azure, Slack, Jira, etc.).
3. Test maintenance: OwlityAI is all about freeing your team from manual overhead. It uses self-healing to adjust or remove flaky tests. 

Step 3. Prepare your team

Whether you plan adopting AI testing tools completely or just want to automate regression testing, your team must understand how to get the most out of them and how they fit into your workflow.

Preparation methods

1. Hands-on training: Live demos, workshops, practical sessions — any activity where testers can see AI in action.
2. Internal knowledge sharing: Are old-hand testers here? Let them mentor others and share insights on AI in software testing.
3. Shadowing experts: Two options: either you partner with external teams or pros already using autonomous QA or you pair internal experts with junior-middle QA specialists and conduct a shadow day (when a pro shows how to work in a particular area, niche, or how to use a specific tool).
4. Calming practices: Many tech specialists worry that AI will replace them. So, it makes sense to run internal experiments showcasing how AI assists rather than replaces humans.

Also, you could do with any kind of learning materials: video tutorials, user guides, and documentation on best practices.

Step 4. Start a pilot

Full-scale adoption right away is brave. And a bit silly as you want to transit smoothly. Instead of lighting-fast adoption, choose a well-defined project (a specific regression suite or UI tests) for a single feature. This allows to:

• Validate how the tool integrates with existing workflows
• Identify potential challenges before scaling
• Measure the impact of autonomous QA in terms of speed, accuracy, and maintenance reduction

Step 5. Scale gradually

Once successful, expand the AI practice across projects — more test suites, more apps, and more teams.

Here are the top signs that show everything is going right:

1. Stable and reliable test execution: Fewer false positives and self-healing test scripts.
2. Faster release cycles: Reduced testing time AND software quality relevant to your current roadmap spot.
3. High adoption rate: Team members actively using the tool and integrating it into their daily workflows.
4. Increased defect detection rate: AI finds more bugs earlier in the development cycle.

Overcoming common challenges in adoption

Fear of complexity

Myth: Autonomous QA solutions are too raveled for beginners.

Reality: Of course, there are different tools for different professionals. But mainly modern AI-powered testing tools are accessible even to teams without QA experience. OwlityAI, for example, provides an intuitive no-code interface, and you can tune the tool to create and execute test cases without coding knowledge. Clear documentation and guided onboarding add up to that.

The mentioned Katalon’s report reveals that 45% of companies cite a lack of experience and relevant skills as the main roadblocks to the adoption of modern tools.

Resistance to change

Solution: Throughout history, humans have been led by leaders. Therefore, use the power of leadership, influence, and team members’ own participation.

Also, you could do with quick wins and tangible results:

• Reduced workload: AI handles repetitive testing tasks that previously drained humans.
• New areas for growth: With more free time, testers can dedicate themselves to strategic objectives, growing with every uncommon task.
• Faster feedback cycles: Rapid insights improve DevOps efficiency and ensure faster fixes and eventually faster time to market.
• More stable test suites: Once again, self-healing frees time and instills confidence.

Other effective methods to overcome resistance:

1. Measurable wins and their celebration — Pilots can showcase quantifiable improvements faster (e.g., test execution time reduced by XX%), this way, ensuring the reason to feel proud. Important: celebrate these wins. The positive reinforcement principle works for sure.
2. Internal champions — Don’t limit yourself to company leaders. Use key team members to advocate for the solution, provide hands-on guidance, and drive adoption. It’s kind of win-win: you grow future leaders and you get what you expected.
3. Constant and friendly communication — Maintain open lines of communication throughout the adoption process. Update your team on progress, address new concerns, answer questions.

Budget concerns

Solution: Long-term ROI of AI-driven quality assurance a priori wins over situational and chaotic approaches.

AI testing reduces:

• Manual testing expenses: Less reliance on labor-intensive test execution and maintenance.
• Regression testing time: AI runs tests simultaneously.
• Bug-fixing costs: The earlier you fix bugs, the less money you spend later.

World Quality Report 2024 states 68% of companies are using GenAI, particularly for testing needs. Over the third prioritize automation investments in the nearest future.

How OwlityAI simplifies autonomous QA adoption

Ease of use

No code, no problems. The intuitive interface allows even non-tech staff to create relevant test cases. OwlityAI has pre-built test templates, yet it deeply analyzes your app’s peculiarities and leverages self-healing to automate complex workflows — refining tests in case of their inapplicability, for example.

Support

We have not had a case when the one hadn’t figured out how to use OwlityAI. Really. That’s why:

• The product has onboarding materials: Step-by-step tutorials and guides for quick learning.
• We offer customer support: Technical assistance to address queries or preventive demos to show typical how-tos.
• OwlityAI doesn’t require previous experience: All best practices and expert insights are in-built. You just need to paste your web app link to the tool. Everything else is on it.

Adaptability

Integrates with almost all common and critical tools:

• CI/CD pipelines: Jenkins, GitHub Actions, and GitLab CI/CD.
• Test management tools: Jira, TestRail, and Xray.
• Communication: Slack, Jira (as a collaboration tool).

Best practices for successful adoption

Collaboration

AI in software testing is the best thing since sliced bread. And one of the reasons is that it contributes to an environment where QA engineers, developers, and product managers work in sync. No gaps — no misunderstanding and, hence, no delays.

Quick and hands-on how-to

• Shift-left testing: The tool performs tests earlier in the development lifecycle and consequently catches bugs before they escalate.
• Unified test management: Testing insights are in your Jira board, visible and clear-cut.
• CI/CD alignment: Tests should run within automated pipelines with immediate feedback to devs.

Analytics

The next-gen approach generates data-rich insights that go beyond pass/fail reports. For example, OwlityAI analyzes trends, behavioral patterns, and defect root causes, and provides actionable insights for future refinement.

Best ways to use AI-driven analytics

• Identify high-risk areas: AI highlights frequently failing test cases or unstable modules and assigns them priorities, but you can prioritize them another way.
• Optimize test coverage: Have redundant test cases? AI will spot them and suggest actions.
• Predict defects proactively: AI-based anomaly detection spots deviations in system behavior.

Refinement

It might seem easy as pie, but it’s not. At least, not at the beginning. You should fine-tune and tailor the tool’s usage to your needs and then steadily refine it to maximize its impact. AI models improve over time, but only if teams iterate based on real-world feedback.

Effective QA strategy: Tips

• Update database: If you use an advanced tool, keep it aligned with evolving test cases and system changes.
• Fine-tune automation workflows: Analyze performance metrics and adjust configurations.
• Expand automation coverage wisely: As we said before, critical workflows first. Then — expand into other areas.

Bottom line

Autonomous QA for beginners might seem overwhelming. Yet, with the right focus (on collaboration, deep analytics, and ongoing refinement), you can achieve faster releases and reduce testing costs by 93%. As a final stage — significantly better software quality.

OwlityAI stands for simplicity and effectiveness. That’s why we chose a no-code interface, seamless integrations, and deep-dive yet clear-cut analytics. Whether you just started adopting AI testing tools or looking to enhance your existing automation, OwlityAI has got you covered. 

Schedule a demo to see how autonomous QA can transform your testing strategy.