How to use AI for your QA process

Software testing evolves alongside software development. They say testing must be even more “bulletproof” than development if we want to end up well. And its evolution shows this clearly. Traditional automation popped up, enhancing QA practices, and DevOps transformed development pipelines a decade ago.

Netflix wasn’t a tech hero the time they started. But being open to tech advancements, they are now able to deploy hundreds of builds daily, most of them autonomously. This is how they raised the bar and created a smooth, entertaining experience.

Autonomous testing and QA symbiosis has introduced a shift, offering QA teams tools with AI and Machine Learning to spot anomalies, prioritize areas for testing, and even self-heal broken ones.

You don’t use AI testing, do you? Meanwhile, the AI testing market is expected to hit USD 2,03B evaluation by 2033 with a CAGR of 16.9%.

Faster feedback loops, enhanced accuracy, and reduced manual effort make us frustrated why so many QA teams are grappling with old-fashioned approaches. Maybe they fear novelty, maybe they just don’t know how to incorporate these advanced tools into their existing workflows.

Anyway, we know how. Let’s break the process down, one bite at a time.

Understanding the role of autonomous testing in QA

What is autonomous testing?

This is when you just press a button, and a pre-trained model puts all testing efforts itself. Then, it shows clear-cut graphs and tables with detailed insights and suggestions for testing strategy adjustments. And finally, it adjusts the testing process itself, based on an ongoing analysis of user behavior within your app.

AI and machine learning are this pre-trained “model” that optimizes and automates testing tasks. It identifies test cases, predicts potential defects, and adapts to system changes.

When you update the app's interface, an AI tool scans all moved UI elements and “fixes” related tests. This intelligence makes it fundamentally different from traditional automated testing, which relies on static instructions and is prone to failure.

Benefits of autonomous testing

The efficiency of this approach is good enough but is not enough. There are many more leg-ups on the traditional testing approach.

• Larger test coverage: Autonomous tools analyze codebases, user behaviors, and application logs to identify areas often overlooked in manual or automated testing.

• Faster, scalable: Next-gen systems can execute a regression test case in hours while it’d take days to complete it manually.

• No need for manual effort (almost): QA engineers can focus on strategic tasks like exploratory testing and root-cause analysis because the AI/ML tool handles the routine.

• Non-stop testing: Integrated into CI/CD pipelines, AI tools ensure tests run dynamically with every build or change.

Preparing for integration

Assess your current QA practices

The first step toward integrating autonomous testing is the evaluation of your current QA process. This revision should focus on identifying weak areas which next-gen testing can strengthen the most.

But we know how hard it could be to examine the area that’s alien to you. For this reason, arm yourself with the following three practical frameworks:

1. GAP analysis: Compare the team’s current performance with desired outcomes on several appropriate metrics. This way, you’ll identify gaps in test coverage, efficiency, and defect rates, and pinpoint specific areas where autonomous testing can fill in the blanks.

2. Swimlane diagram: This approach visualizes the workflow by separating tasks into lanes based on roles or phases in the QA process.

3. Value stream mapping (VSM): Another visual method with the only difference — it creates a clear-cut image of the entire software delivery process, from development to deployment.

They say, there is an app for that, so don’t shy away from additional tools. Lucidchart, Miro, or Tasktop are helpful for visualizing workflows and dependencies.

Define integration goals

You can’t achieve the thing you can’t describe clearly. When we’re asked about the most important part of software testing integration, we always say goal setting. Understanding what you want to achieve with autonomous testing will guide you.

Usually, our clients bring up the following goals:

• To reduce manual effort: Small teams have not enough resources and time to spend on testing. So they might be interested in cutting this bit of work.

• Stretch test coverage: Mature organizations have more resources and more sophisticated apps, so they need more accurate detection within particular modules.

• More accurately spot defects: Enterprises usually release more often and they just don’t want to bog down in routine regression testing. At the same time, they want to decrease bugs in the post-release stage to ensure users are not affected by changes.

Select a relevant autonomous testing tool

Considering all the features of AI-powered testing tools, it’s like a full-fledged team member. Therefore, it must fit seamlessly into your existing workflow and other team members (learning curve). Choosing the tool, check these factors:

• Compatibility: CI/CD pipeline integration, seamless cooperation with bug-tracking systems and existing test management tools if you need both.

• Ease of integration: How long will it take to deploy and configure?

• Support for your testing needs: Ensure the tool accommodates UI, API, and performance testing, or any other types critical to your product.

Steps to integrate autonomous testing with QA

Pilot before committing and scaling

It might be scary to move to something new if your current system works, even though it works not as effectively as you want it to.

Yet, you don’t need to commit from the outset, start a pilot project. Focus on areas where autonomous testing can create immediate value. For instance, regression testing. This type is based on repetitive tasks and has a critical role in ensuring stability after updates.

The chain of actions is simple:

1. Choose a manageable project scope (a single module).

2. Set specific and clear key metrics (execution speed, defect detection rates, or resource savings).

3. Run the new testing process and check metrics every time you run the cycle.

Results will serve as proof to stakeholders and as a guide to you.

Expand the system

Hit the target? Then, expand autonomous testing across more areas of the QA process. Here is where you can continue:

• Unit testing: To validate small, isolated pieces of code and allow developers to receive immediate feedback on their code changes.

• Integration testing: To ensure newly introduced components work seamlessly together.

• Functional and system testing: To validate the application’s behavior in more complex scenarios; end-to-end testing in other words.

The step-by-step approach minimizes risks and ensures that team members have the chance to adapt to novelties.

Integrate with existing tools and processes

There are no QA automation strategies without changes and advancements. In return, any tech improvements require new ways of co-existing with the previous approaches. Your old tools should work smoothly with the new ones. So, when you’re implementing an AI testing tool, ensure to connect it with:

• Test management systems (e.g., Zephyr or TestRail) for unified reporting

• CI/CD pipelines (e.g., Jenkins or GitLab) for continuous, automated deployment.

• Defect tracking tools (e.g., JIRA or Bugzilla) to close the loop between issue detection and resolution

As well, ensure robust APIs or pre-built connectors exist between your testing tool and these systems to avoid data silos.

Control for proper feedback loops

Regularly reviewing the scope ensures prompt process revisions. Gather QA engineers, developers, and those responsible for autonomous testing implementation to analyze discrepancies, share insights, and identify areas for manual validation.

By the way, AI tools commonly have analytics and reporting features. For example, you can create dashboards that highlight trends to make informed decisions.

Monitor and optimize

At the moment, any integration is not a constant thing that you’ve completed and forgot about it. You should control and refine your strategies looking for where the AI-powered testing tool can do even more good.

When using analytics, keep an eye on the following metrics:

• Execution times for different test scenarios.

• Coverage growth across new features.

• Defect detection rates in production environments.

Schedule regular optimization cycles where QA leads analyze these metrics and fine-tune both the tool and testing strategies.

Overcoming common challenges

Resistance to change

This one has been the most irritating for business owners for decades. Introducing any improvement often faces pushback from team members who are used to traditional methods.

Unfortunately, we have only sad news here: modern resistance will become even worse with years since AI evolution and related fears like job security.

Previously, the main resistance reasons were a lack of familiarity with new tools or skepticism about their effectiveness. Now, 54% of tech companies in the US are using AI for coding, and 33% are sure that GenAI will transform their companies within a year.

That means that skepticism transforms into a fear of job security.

Six strategies to overcome this and other challenges:

1. Provide training: Hire an expert or purchase a hands-on workshop on using autonomous testing tools (would be better to focus on a particular one). The key is to eliminate the fear of the unknown.

2. Enable quick wins: Small, early successes are the most powerful instrument for any endeavor. Time savings or defect detection improvements in pilot projects can build confidence in the new approach.

3. Create a diverse team: Gather cross-functional teams, include tech and non-tech specialists. Relevant ones, of course. Zero in on their feedback; this will create a sense of ownership.

4. Celebrate success stories: Share real-life examples of QA teams benefiting from autonomous testing. Highlight case studies or testimonials.

5. Address job security concerns: Many tech visionaries are now saying that AI won’t take our jobs, but those who use AI will. Explain that autonomous testing aims to enhance team members’ roles. Highlight that AI testing creates more time and space for more meaningful work.

6. Focus on career growth: Building on the previous point, emphasize how mastering AI-driven tools can enhance teammates’ skill sets. Such openness can even create a more healthy atmosphere in the company since colleagues will see that despite being valuable, they are not supposed to work for you forever.

Ensuring quality and accuracy

Experiments in enhancing QA practices can lead to skepticism and doubt. That’s why validating the accuracy of autonomous testing is your bread and butter as a tech leader. To build trust in the new system:

• Cross-reference results: Don’t refuse manual testing. Compare AI testing findings with manual testing results.

• Use dual reviews: Require both manual and automated reviews for high-priority test cases.

• Run parallel tests: Why don’t run both types of testing (if your resources allow this, of course)? This way, you can identify discrepancies and refine the system.

Balancing automation with manual testing

A hybrid approach discussed above creates a space for negotiation and innovations. AI excels in repetitive and labor-intensive tasks like regression, performance, and load testing. Traditional testing is still useful for:

• Exploratory testing: Investigating new features or areas not covered by predefined test cases.

• User experience validation: The app’s performance meets user expectations.

• Complex or edge-case scenarios: Handling nuanced conditions that require human intuition.

It’s all about your vision and approach. Autonomous software testing might handle the lion’s share of repetitive tasks, and human testers zero in on high-value, creative ones.

Bottom line

Autonomous software testing allows for achieving 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. Resistance to change and other challenges may interrupt the integration process, but don’t fear — leadership example, learning opportunities, and job-securing practices will help.

OwlityAI is the next-get AI-powered testing tool that makes testing faster, more robust, and smoother. With advanced analytics and effortless fine-tuning, it emerges as a go-to instrument for startups, mid-sized businesses, and flexible enterprises.

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