Top reasons why autonomous testing should be your next investment

65% of companies surveyed by McKinsey are already using AI, and in the Middle East, the GenAI economy could soar to USD 24 billion per year by 2030.

At the same time, we’re all seeing this increasing pressure to deliver high-quality applications faster than ever. The mentioned consulting giant also estimates AI could generate USD 2.6 trillion to USD 4.4 trillion annually across the 63 use cases, including AI-driven QA solutions.

Autonomous software testing is a transformative solution to meet modern demands. It overcomes the limitations of traditional and automated testing methods. Autonomous testing adapts in real-time, learns from each test cycle, and optimizes itself without constant human intervention.

Further, we outline the top reasons why an AI-powered testing tool should be your next strategic investment. Backed up by data-driven arguments and real-world examples we demonstrate how adopting this technology can enhance software quality and give your team a competitive edge.

The growing demand for speed and quality in software development

Let’s face it — it’s not accidental that software developers’ burnout rate is one of the highest across all industries. Users expect a better experience with every release or update. Without agility and responsiveness, the tech side of your business is doomed to failure. Like Cyberpunk.

Failure example: Cyberpunk 2077

Recall December 2020: gamers all over the world were roaring to play Cyberpunk 2077. But launched with significant bugs, the game brought about significant criticism for CD Projekt Red. The game’s inflexibility and inability to meet modern demands led to:

• Massive refunds: Sony removed the game from the PlayStation Store with full refund offers.

• Reputation damage: While the game developer studio secured some next projects, this failure also had consequences.

• Financial losses: If you check CD Projekt Red’s financial report, you won’t notice a significant drop. However, a deeper dig will show hundreds of negative reviews which just couldn’t have gone unnoticed.

You may think that the main takeaway is the developer studio’s sloppy attitude. But it is not. The prior conclusion is that modern customers demand unprecedented-quality software, which creates a huge pressure on developers.

Success example: Zoom

Now, let’s look at the mirrored example. Many might say that the chance played for Zoom. We can’t help but admit that the COVID-19 pandemic has really helped the company. Yet, their flexibility and early adoption of new tech breakthroughs allowed them to scale their services. What did exactly (apart from a fortune) help them?

• Cloud: Thanks to this relatively new approach to operating data, they handled a 20-fold increase in traffic in just three months.

• User-centric approach: Carefully listening to user feedback allowed them to improve reliability and ease of use.

• Continuous deployment: Implemented agile methodologies to roll out security updates and new features quickly.

As a main takeaway from this story: Real success doesn’t come on its own; it requires a flexible mindset, readiness to experiment, and adoption of new technologies quickly.

Challenges posed by demand

As you see in the examples above, software teams face many challenges that sometimes go beyond their main responsibilities. Namely:

• Need for continuous testing: Testing must keep up with frequent code changes to meet deadlines.

• Shorter development cycles: Traditional end-phase testing is impractical in agile environments.

• The extra mile: Quick issue resolution is not enough now. You must do more than your competitors to meet user expectations.

The limitations of traditional testing

Traditional testing is akin to a heavyweight runner. They may succeed in boxing but running requires some “software testing investment”: lighter constitution, flexibility, and speed.

Even conventional automated testing requires significant maintenance as applications evolve. One underlying issue from the provided examples is the reliance on traditional testing approaches that couldn’t keep up with the tight deadlines:

• Insufficient test coverage: Critical bugs slipped through due to inadequate testing of various platforms.

• Delayed feedback loops: Slow testing cycles delayed the identification of performance issues.

• Inflexible processes: The inability to adapt testing strategies quickly hindered the team’s responsiveness to emerging problems.

When not aligned with modern development needs, traditional testing leads to bottlenecks and increased costs. This is where AI in software testing comes into play.

What is autonomous testing?

It is refreshed testing that handles “the dirty work”: test creation, execution, and maintenance. No need for a broad QA team, no need to spend hours on test creation and maintenance. The main end result is saved time and a better software quality.

Previously, companies achieved these results using automated testing — the next step from manual testing. But let’s face it, this approach needs predefined scripts and regular updates, and AI-driven QA solutions adapt to changes in the application automatically.

AI testing has six core features:

1. Self-generation: The tool scans the app’s behavior and users’ interactions with it and creates test cases — developers don’t have to write tests manually.

2. Self-healing and resource optimization: The app is a dynamic organism with many ongoing changes. With every change, the autonomous tests change too. This feature is called self-healing. Moreover, the tool also prioritizes critical tests and efficiently allocates resources.

3. Adaptive learning: Each test run is the learning material for the tool, which improves its understanding of your app. The main effect is the increasing ROI of AI testing due to its flexibility and adaptability to new features/changes.

4. Defect detection: Previous test outcomes are also the subject to learn from. The tool scans and scrutinizes them to detect anomalies and potential defects that might not trigger traditional test failures.

5. Continuous integration: Autonomous testing tools integrate seamlessly with CI/CD pipelines. Do you commit code? The tool triggers tests immediately.

6. Analytics and reporting: Visualized detailed insights, code coverage, and performance metrics on a single dashboard.

Automation → autonomy shift

The shift took some time to allow us to reap the benefits of autonomous testing. It was not rapid, but it was pretty clear that apps would become more complex and quality assurance would need acceleration in several years. Here is how the path was paved:

First phase: Manual testing

How were thighs at the very beginning? Developers had to write code and separate tests for each potential pitfall/issue/case. Manually. Then, testers executed tests step by step, also by hand. This method took a lot of time but still didn’t guarantee the flawless performance of the application. Nothing to say about scalability issues — when the app became more complex, it was almost impossible to test it thoroughly.

Second phase: Automated testing

Quite obviously, developers needed at least simple automation. This is how predefined scripts appeared — testers wrote scripts using automation tools to automate interactions with the app. This improved efficiency and consistency, and decreased human errors a bit.

The thing was that automated testing still required a human effort: writing and maintaining scripts could take days or even weeks.

Third phase: Autonomous testing

And here is the next leap, with a smart testing process. AI in software testing goes above and beyond translating into three steps:

• Step 1: The model generates tests automatically based on the application’s current state and user behavior patterns.

• Step 2: You don’t need to start tests manually — every code commit makes this automatically.

• Step 3: Any change forces the system to reflect them and adjust the testing process.

Top reasons to invest in autonomous testing

Higher efficiency and speed

It’s simple: The autonomous testing tool automatically generates and executes tests, which allows for faster issue identification and fixing. Autonomous testing adoption accelerates the development cycle and brings about quicker releases.

Enhanced test accuracy and reliability

This new approach to testing eliminates human error and increases the accuracy of test results. Tools like OwlityAI continuously learn and adapt, improving test accuracy with each attempt.

This ensures effectiveness even after the next step in the app evolvement — defects that traditional methods might miss will be spotted. The intelligent algorithms also identify edge cases and enhance the overall reliability of software.

Take Salesforce as an example. The giant employs three key strategies:

1. They verify output

> They identify where defects might occur and proactively monitor this spot.

> They do such validation in real time.

> This is their main risk mitigation strategy for incorrect test generation.

2. Performance tracking

> Advanced observability tools.

> Testing performance is constantly checked.

> Granular insights into testing processes.

3. Day-to-day learning

> Rectifying retrieval and generation errors.

> Analyzing prompt performance and effectiveness (for areas beyond testing).

> Scanning and retrieving insights from historical data to develop new testing strategies.

It is cost-effective

Your next software testing investment should be in AI testing tools. Why? Because a stitch in time saves nine. They reduce the need for extensive manual testing resources so that you need a smaller QA team to meet your goals. How about maintenance? They also decrease alongside post-release defects.

As per several cases, AI-powered testing tools x2 the ROI within three years by minimizing manual efforts and accelerating release cycles.

Future-proofing your QA strategy

If you don’t want to fall behind, you should create trends by noticing their spring-throughs. Autonomous testing is one of such. Next-gen testing tools support continuous integration and delivery, making it a forward-looking investment that keeps your QA strategy relevant.

> Adaptability to new technologies: Modern companies can’t think only about today; they must have a strategic view and a clear understanding of the new milestone. Their ability to handle emerging technologies depends on it, and hence, their competitiveness.

> Competitive advantage: This move puts you ahead of competitors—at least before they adopt the tool, too.

Example: Airbnb’s approach to AI

Airbnb invested in AI, including autonomous testing. From the outset, they implemented several AI features like quick AI-generated responses to hosters and smart photo organization. Back to testing, they achieved:

• Seamless integration with DevOps: Enhanced collaboration between development and operations teams.

• Preparedness for growth: Positioned to handle future technological changes.

• Customer experience is king: Maintaining high-quality releases, they try to boost user satisfaction and higher rankings. This is especially relevant due to the rapid evolution of AI-powered travel agents.

Improved team productivity and morale

Automating routine tasks helps your development and QA teams develop a strategic mindset and shift focus. This can improve team morale, as team members will feel appreciated and valued. They will feel that they actually make a tangible contribution, not just “moving numbers in an Excel chart.” Namely:

• Reduced burnout: Minimizes tedious tasks that lead to employee fatigue.

• Enhanced innovation: Teams can zero in on developing new features and improving the product.

• Better retention rates: Satisfied employees are more likely to stay with the company.

Haystack repealed that 83% of developers, testers, and other IT specialists feel burnout from work. How in your opinion can you decrease the stress level for your employees? Autonomous software testing is a bold option.

Enhanced security and compliance

Security testing and compliance checks are often a part of regular testing cycles. The key is to identify vulnerabilities early to mitigate risks and ensure adherence to regulatory standards.

AT&T, 23andMe, a UK pathology lab Synovis — Y24 was packed with freak security breaches. Integrated autonomous security testing addresses these issues early and saves companies from costly compliance fines.

How to get started with autonomous testing

AI-powered testing is becoming a game-changer in optimizing quality assurance processes. For example, American Express adopted NVIDIA AI solutions to prevent fraud and ensure compliance. As a vendor stated, AE accelerated time to market and achieved a x50 better performance in production, considering established metrics.

AI testing is not just a trend — it’s a strategic move, and you want to adopt it if you intend to stay competitive in the next 5-7 years. How exactly?

Evaluate your current testing process

1. Audit your test suite:

• Comprehend how things look like at the moment. Document it: mind maps or charts are the best for this.

• Spot which tests take too long or frequently fail.

• Mark these tasks and the repetitive ones as an entrance point.

2. Identify high-impact areas:

• Pinpoint components or features with a history of bugs or frequent updates.

• Focus on areas where testing is always (or just often) failing to meet its goals.

3. Get your ducks in a row:

• Collect important metrics: test execution time, coverage, and defect rate.

• Organize the information to understand where autonomous testing can bring the most value.

Two quick tips

1/ Engage your team early

> Involve developers and QA engineers in the assessment. Non-tech specialists are also helpful sometimes (it depends on their expertise and previous experience).

> Encourage them to share insights on the challenges they face.

2/ Set clear objectives

> Define what you want to achieve; use the SMART framework.

Choose the autonomous testing tool: Criteria

Why OwlityAI

Because the tool meets all expectations and exceeds in proactivity and vendor commitment to excellence. Namely:

Seamless integration:

• Compatible with GitHub Actions, Jenkins, and other major development tools and CI/CD systems.

• Minimal disruption to your existing workflows.

User-friendly interface:

• Designed for ease of use, enabling quick onboarding.

• Visual dashboards and intuitive controls simplify test management.

Robust AI foundation:

• Automatically adjust to changes in your application.

• Creates comprehensive test cases on its own.

Scalability:

• Handles projects of any size, from startups to large enterprises.

• Supports web testing web; mobile and desktop apps are coming soon.

No need for QA expertise:

• You can have a smaller QA team (actually, you need just one specialist) and save on operational costs.

• Your supervisor isn’t supposed to be a QA guru. They don’t need a deep testing experience at all.

Planning for a smooth transition

Every founder or C-suite representative thinks by numbers. To achieve the highest ROI of AI testing, plan carefully and execute wisely. Save this memo below for a successful transition.

Team training

• Organize workshops: Help the team to adopt the new tool. Hold a webinar to cover essential features and best practices.

• Form a pioneer team: Gather all enthusiasts in one team and make them ambassadors of the new technology.

• Instill a life-long learning mindset: Above all is access to learning materials and updates. Listen to your team members and lead by example.

Gradual implementation

• Pilot before going all in: Choose a non-critical module to test the waters. Allows the team to learn and adapt without stress.

• One more time on clear goals: Define success metrics and use them to gauge progress and make adjustments.

Iterate and expand

• Conduct a retrospective of the pilot to refine your approach if needed.

• Gradually extend autonomous testing to other areas of the project.

• Monitor defect detection rates, test execution times, and other KPIs to identify trends and areas for improvement.

Bottom line

Let’s face it, AI in software testing will not whoosh by; this technology is becoming the next big thing. It’s worth sharing the benefits and goals of autonomous software testing with all stakeholders and aligning across the company on the vision.

But it is all about the work with your team. Train them on how to use the new tool, acknowledge milestones and improvements, recognize progress — all these steps are crucial to showing the real value of AI-driven QA solutions.

Start by evaluating your current capabilities. For all the next steps, there is the OwlityAI team. Contact us for a free introductory meeting or directly request the demo, if you are sure it’s a match.