How to integrate autonomous testing with existing QA practices

Airbnb knows what the change for the better actually is. This is when autonomous testing and QA work hand in hand, improving the overall NPS and satisfaction score of your product. Some sources state that the tech travel leader increased repeat bookings by 20% after implementing AI in their processes.

This strategic step implies: if you don’t use AI and even don’t consider integrating autonomous testing, you won’t win this game.

Manual testing is a time-consuming and error-prone process. Conversely, AI-powered testing is becoming a go-to solution for maintaining a competitive edge in software quality.

AI testing tools generate test cases, predict potential failure points, and execute complex test scenarios with minimal human intervention. Backed up by machine learning, they adapt, learn from previous test runs, and continuously improve.

If you are ready to start off, calculate how much OwlityAI can save for you, or keep reading — we’ll guide you through the process of AI software testing integration with existing QA practices.

Understanding the role of autonomous testing in QA

What is autonomous testing?

It’s the next big thing in QA automation strategies. With AI and machine learning, next-gen testing tools optimize and automate various software testing tasks: analyze application behavior, create relevant test cases, update them and, of course, learn from previous testing outcomes.

These tools generate and execute test cases without overwhelming the QA team. This approach enhances testing efficiency and, hence, software quality. As a side effect, testing and dev teams feel less burnout and more happy with the results of their work.

Autonomous testing vs. automated testing

Benefits of autonomous testing

AI testing embodies several key features, enhancing QA practices and standing as a modern solution for businesses of all sizes and tech levels.

• Increased test coverage: Testing tools generate and execute a broader range of test cases (regular interactions, accidental actions, and edge cases) — a manual QA team could miss bugs and considerations with such a volume. This comprehensive coverage helps identify defects earlier in the development cycle.

• Faster execution: Acceleration allows teams to deliver updates more quickly. Automating repetitive and time-consuming tasks is a secret sauce here.

• Less manual effort: This approach minimizes manual intervention in each stage. Yes, it still needs some control and initial setup at least, but they are effortless. And the reduction in manual effort frees up QA engineers to focus on more strategic tasks.

• Continuous testing is in the game: Autonomous testing supports continuous integration and continuous delivery (CI/CD) practices: testing is happening throughout the development lifecycle. A significant booster for catching bugs, like a wider net for fish.

• Quality is way better: The technology learns from previous test runs and adapts accordingly. This base in AI-powered testing tools improves the accuracy of defect detection. This, in turn, leads to a higher (and stable) level of user satisfaction.

Preparing for integration

Assess your current QA practices

Mentally step back and examine your existing QA processes from the outside in. All inefficiencies, bottlenecks, and repetitive tasks will be easy to spot, making it much easier to identify areas where autonomous testing can deliver the most value.

Frameworks for evaluation

Value Stream Mapping (VSM): Usually used in manufacturing, this lean management approach helps visualize the flow of materials and information through your QA process. Map out each step and identify waste and areas for improvement — in these dips you can potentially use AI testing in the next steps.

SWOT: Strengths, Weaknesses, Opportunities, Threats — a very common practice, doesn’t need introduction.

Process maturity model: Breaks down processes into stages from initial (chaotic and unstructured) to optimized (well-defined, measured, and continuously improved). It also draws a clear pathway for integrating autonomous testing.

Define integration goals

As any other endeavor, AI-powered testing also needs clear and measurable goals. The SMART framework (Specific, Measurable, Achievable, Relevant, Time-bound) is an excellent tool for this reason.

You want to reduce manual testing time, increase test coverage, or improve defect detection rates — dive deeper [and check our other articles where we have covered all aspects of next-gen testing].

Back to goal setting: Instead of a vague goal like "improve testing efficiency," define a SMART goal — "reduce manual testing time by 25% within the next quarter without extending the team."

❗Important: These goals must be in balance with broader organizational objectives. If your company aims to accelerate release cycles, your integration goals should target testing speed and reliability.

Choose an appropriate autonomous testing tool

The choice of the right tyres for a racing bolide determines 50% of its future success in the race. And a proper picking of the testing tool influences the outcome of your endeavor by all 90%. So, don’t shy away from detailed analysis:

• Tool compatibility: The tool should integrate with your current testing frameworks and environments.

• Ease of integration: Does your “candidate” offer a straightforward setup and minimal disruption to existing workflows?

• Support for testing types: Functional, regression, performance testing — the tool should perform all types you need at least for web apps.

Why OwlityAI

You provide data, and OwlityAI analyzes the app and identifies functionality for further testing. Apart from auto-scanning and test scenario generation, our tool has several helpful features:

• Smart prioritization: Assesses test cases and dishes out priority to them (High, Medium, or Low). Obviously, further execution starts with high-priority cases.

• Ongoing test maintenance: Monitors UI changes and automatically updates test cases and scripts as the application evolves.

• Inefficient test detection: Catches unstable tests, resolves issues, and re-runs them for reliable results.

• Parallel “cloudy” execution: Executes tests simultaneously in multiple threads. All in the cloud.

• CI/CD pipeline integration: The tool integrates directly into your CI/CD pipeline (Jenkins, GitHub Actions, GitLab — you have everything to tune the new testing process with top integrations).

Steps to integrate autonomous testing with QA

Inconsistency and zig-zagging between approaches might become your most dangerous rival. Gartner’s 2023 CIOs Guide revealed that 80% of CEOs have doubled down on tech investments (and McKinsey seconds that). Yet, CIOs still fall short of the mark. Follow a simple 5-step strategy to escape the same fate.

Most companies stated they had received some or great benefits from digital transformation efforts

1. Pilot before going all in

Autonomous testing and QA can go hand in hand but also step by step. To lower the risks and make your plan come off, begin with a pilot project. Select a specific project where the new approach can make either the biggest or most immediate impact. Consider regression testing. Here’s why it’s worth starting off this way.

Why regression?

Because you should ensure new changes don’t break the app, and regression testing is exactly for this purpose. Also, it implies repetitive tasks which are time-consuming and prone to human error. As well, regression testing:

• Requires frequent execution, so AI has a space to speed up testing cycles.

• Directly impacts the overall software quality.

2. Once succeed, scale

Gradually expand the scope of autonomous software testing. Smoothness at its best:

• Unit testing: The new tool will identify errors early and reduce debugging time later.

• Integration testing: Validate interchanges between components: AI tools quickly execute integration scenarios.

• System testing: Validate the app’s overall functionality and performance.

Some hands-on insights:

> Low-hanging fruit: Spend more time on research and deepening your understanding to save your future time. Focus on testing areas with repetitive tasks that don’t require alignment and extra discussions.

> Documentation: If you are a company’s top, you are not eternal. Someone will replace you in some time. Make sure you facilitate a smooth transition to autonomous testing and allow your successor to work without the pain in the neck. Also, testers will need it to fine-tune the new testing tool.

3. Integrate with existing tools and processes

Ensure that your AI-powered testing tool integrates with your current test management system to report test results and centralize test case management.

• Ensure CI/CD pipeline integration: Tests are automated and executed with each code commit or code base change.

• Use defect tracking tools: Autoreporting saves time and effort (especially if you have a buggy code base).

4. Ensure prompt feedback

The next-gen approach doesn’t mean you should give up manual testing completely – it’s also important – but you also should ensure proper feedback, as it allows the testing process to adapt and evolve.

To tune a prompt feedback mechanism you don’t need to reinvent the wheel:

> Regular review meetings: Schedule repeated meetings where cross-functional teams can discuss what’s working, what’s not, and how results can inform future testing strategies.

> Automated reports: Generate insights on test performance, coverage, and defect detection rates based on tangible outcomes and data.

5. Monitor and optimize

Make sure your testing tool has analytics and reporting features to track progress and optimize testing strategies.

Otherwise, use analytics tools to assess the performance. Constantly check main metrics: defect density, test execution time, and coverage rates.

Stay adaptable in your approach to integration: regularly ask for feedback from teams about their experiences with autonomous testing and make necessary adjustments.

Overcoming common challenges

Resistance to change

Adopting any new things is difficult, so, as a leader, you will always encounter resistance from people who are used to particular practices. The team may feel apprehensive about changes or fear that a new practice could render their roles obsolete.

But be careful — don’t try to persuade your team by word. To overcome this resistance, you could use these strategies:

• Training and resources: Training programs can show that new technologies are not as scary as they seem. Conduct workshops or seminars where team members can learn all QA automation strategies and their stages of implementation.

• Results — now: Early successes achieved through autonomous testing are paramount, especially if we are talking about teams with the younger generation of testers. For example, if automated regression tests significantly reduce execution time for a particular release, pack this achievement in an attractive format (e.g., infographics) and share it with the team.

• Create cross-functional teams: Don’t limit yourself to only tech professionals. Engage relevant team members in the planning to build a teamwork spirit. Allow them to voice concerns, suggest improvements, and contribute to decision-making — this way, they will feel valuable and appreciated.

• Define leaders: “Testing champions” can advocate for autonomous software testing. Nothing works better than “internal agents” and their word of mouth.

• Praise publicly, criticize privately: Recognize the team’s achievements, and remember — praise publicly. But if you feel disappointed, provide feedback one-on-one. Don’t forget to celebrate milestones achieved during the integration.

Ensuring quality and accuracy

Validation is even more important in the early stages of integration. You will count on automated testing more heavily, so it’s essential to ensure that the results are trustworthy.

• Cross-referencing with manual testing: Double-check autonomous testing results with manual testing. For example, conduct a series of critical tests using both manual techniques and autonomous tools, then compare the results. This move doesn’t guarantee a 100% positive outcome (as both may not come off accidentally), but it significantly eliminates the error possibility.

• Retrospectives for AI testing: Retrospective overview of ups and downs encourage collaboration and collective problem-solving.

Balancing automation with manual testing

AI-powered testing tools are great. However, there are areas where manual testing remains vital.

• Exploratory testing: Human testers’ creativity and intuition spot issues that automated tests might miss. Nothing is ideal.

• User experience validation: Testers can assess the application’s usability and overall feel from a human’s POV — robots cannot fully evaluate this. Note: protocols for manual reviews can streamline the examination.

• Complex scenarios: Business logic is not always logical. And only humans can understand it (at least, now). Some edge cases also come down to it.

Bottom line

Every business wants to strive for imbalance: to achieve more with less spending. Sounds impossible…if you are in the dark about autonomous software testing. At least in the testing area, any company can achieve better software quality without extending team size and working hours.

Start integrating autonomous testing from the pilot project — the less significant area, like regression testing. Then, scale. You may encounter resistance from your teams, so don’t shy away from practices mentioned in the article — a proven approach rarely falls flat.

If you feel unconfident about AI software testing integration, OwlityAI’s team is ready to help. We don’t want to persuade you but rather help investigate new possibilities with this approach. Book a free meeting with our team, or just start off by hitting the button below.