The top 5 ways autonomous testing will transform your QA team

No-human testing is not futuristic gibberish. AI in software testing is in full swing, actively reshaping how we perceive modern software development and testing in particular. 

Old-fashioned companies rely heavily on manual or partially automated testing and still encounter postponed releases with defects. On the contrary, tech leaders saw the potential of autonomous testing solutions from the outset. That’s why they are leaders, actually. 

Meta, Netflix, Google — sounds like a taunt, doesn’t it? Absolutely not. Let the big companies be, but leverage their vision and tailor their approaches to your size. At least, you can significantly increase QA team productivity which will pay off, hands down.

For this reason, below, we’ll break down the top five ways autonomous testing transforms quality assurance. Whether you’re a big whale in your niche or a well-run SMB, this text will help you understand why you could do with the next-gen testing solution.

1. Speeds up releases by accelerating testing cycles

We can’t call manual testing outdated, but the traditional approach can’t keep up with Agile and DevOps environments. Average things like regression testing and smoke tests turn into bottlenecks and keep you from a more significant competition advantage. Contrarily, autonomous testing creates the desired additional time in the day by automating almost all testing operations.

Key impact

• Shorter test cycles: AI-powered testing tools reduce regression cycles from days to hours alongside accelerating feedback for developers.
• Prompt feedback: These next-gen tools analyze the app in real-time and change the testing system accordingly. This way, the development team gets live info about bugs and can fix it much faster.
• Focus on high-value testing: With total automation, your QA team can bring in more value. While the machine handles the routine, humans come into their own with creativity and intuition. Exploratory testing, performance optimization, and strategic quality improvements — it’d better leverage humans’ qualities in such areas.

2. Improves test coverage and accuracy

How to achieve comprehensive test coverage and maintain its efficiency at the same time? This is one of the biggest challenges in traditional QA. Humans are humans no matter how weird this sounds. They make mistakes, miss some cases and scenarios, and are not as scalable as machines. 

On the other hand, autonomous QA solutions analyze bug logs, previous testing outcomes, their impact on the product, and current app functionality. This helps the tool generate comprehensive test cases, including edge ones, and adapt to changes in the codebase.

Key impact

• Bug-free production: AI tools usually take into account incomparably more specific scenarios than humans. Those who think AI can’t comprehend complex interactions are wrong, to say the least.
• Cost reduction: Autonomous tools work smart and more thorough than humans. They identify high-risk and high-impact areas within your testing process and your app in particular. Then, they assign priorities to each area so that the system tests them in a particular order and developers fix bugs that influence the product the most. This efficient flow reduces wasting time and money spent on post-release fixing.

Why OwlityAI

Smart test generation. Self-healing. Adequate prioritization and rigorous analysis. All these advantages speak louder than words. Additionally, OwlityAI allows you to know beforehand what to expect both in terms of results and in terms of costs.

3. Frees QA teams to focus on high-value tasks

Any approach except AI-based requires constant upkeep. This drains QA time and efforts in the long run. The mentioned AI-based solutions continuously execute and adapt test cases on their own and, this way, free QA engineers to focus on testing strategy and high-risk areas.

Key unobvious impact

• Job satisfaction: A 2024 MBS EE Satisfaction Survey revealed that meaningless routine significantly impacts overall satisfaction. Despite the market conditions lowered dissatisfaction score, it still remains high at about 35-40%. So, if you automate repetitive tasks, rest assured your QA teams will thank you a million.
• Boosts innovation: More time doesn’t mean a more powerful thinking process. However, many teams really spend more time thinking over product improvement. New methodologies, shift-left testing, chaos engineering, model-based testing — with total automation engineers can actually engineer. 
• Increases efficiency: OutSystems and KPMG conducted research and found out that autonomous testing brings about 54% software quality improvement (fewer bugs). This is alongside improving QA team productivity and halving testing time.

4. Fine-tunes collaboration across teams

Historically, software testing has been siloed: development teams were responsible for building and QA teams — for reality checks in some senses. 

The old-fashioned process:

New code → testing → bug reporting → fixing → testing → production (if everything is okay)

The modern approach looks another way. AI-powered QA solutions become an indispensable link in the development chain with testing at each stage of SDLC. Why so?

• Collaboration is smooth
• Communication is clearer
• Software modules tied to other features, they are not separated from the functionality (in terms of overall functionality)
•  Multi-tier checks ensure better quality as you have doubled the chances of catching bugs

Key impact

• Communication: Almost all testing tools have shared dashboards and the capability to share reports, insights, and documents with other team members via any comms tool (Slack, MS Teams, etc.). This ensures visibility and eases prioritization. Yes, your team is again in the same boat. 
• Issue resolution: AI-root cause analysis spots defects at the code level so that it takes less time to debug and less money to fix. Developers receive immediate (instant) and actionable feedback (list of recommendations).
• Integration: Jira, Slack, GitHub, and other tools are at your disposal. All stakeholders have instant access to prepared and processed test data, all executors have clear actions (read as “a plan”) for improvements.

Example in point

An e-commerce company implemented AI-driven test automation across its DevOps pipeline. As we pointed out above, developers obtained real-time test failure diagnostics and automated defect triage. The project cut defect resolution time by 55+% and accelerated its release cycle from bi-weekly to weekly deployments.

5. Future-proofs QA teams

How far do you plan? Do you have a BCP? Does it include a tech matter, particularly testing transformation due to technology advancements?

These questions seem corny or obvious, but, in fact, they are game changers. As the traditional approach requires regular script updates, it almost levels out the possibility to scale fast.  

On the other hand, AI-powered QA solutions work non-stop: they stand on previous thorough test outcome analysis and scan the app in real time so that the system gets the food to learn from. This means the tool will refine the entire process on its own. 

Key impact

• Flexible teams: The key parameter of flexibility is the ability to address any concern proactively. If you are working in the DevOps environment, chances are you will fall behind without proper automation. 
• Reduced defect rates by:

           - Ongoing test prioritization: After the mentioned analysis, the tool identifies high-risk areas and instead of running full regression suites starts the most critical tests first.

          - Self-healing: AI autonomously updates locators and selectors when the UI element changes.

          - Anomaly detection: Tired of illogical user behaviors? AI lees you… and detects such cases as well.

Example in point

Given: SaaS startup at early stages. 

The team opted for AI-driven testing. The new tool scanned previous test results, spotted patterns in failed ones and adjusted test priorities. Result: a 35% drop in post-release defects within a year. 

Overcoming concerns about autonomous testing

Will AI replace QA engineers?

We can’t speak on behalf of the entire industry. However, the current state of things is different. Autonomous testing is not for total replacement but for ongoing help. AI handles repetitive and data-intensive tasks, and QA teams focus on exploratory testing, UX validation, and defining overall test strategy. The testing objective is an equation, and the AI tool is a multiplier.

Is it hard to implement?

Usually, next-gen testing tools are clear-cut and intuitive. They have low-code/no-code interfaces, allowing teams to run the entire process without deep programming expertise. Additionally, there is extensive integration with DevOps pipelines so that any new tool works smoothly with the new one.

How much does an AI tool cost?

Yes, AI-driven testing tools require an initial investment, but zero in on ROI. It’s quite more significant:

• Less manual testing: AI automates repetitive tasks, cutting down on test execution time and costs.
• Faster releases: You identify issues earlier, fix them faster, and rapidly enter the market. Again and again, with the new release, lowering engineering costs.
• Lower defect-related expenses: Fewer post-release bugs reduce hotfix costs and better user retention.

OwlityAI as a solution

Ease of use: Designed for all skill levels, OwlityAI features an intuitive UI, pre-built test libraries, and seamless onboarding to minimize learning curves.

Top features:

• Smart test case generation: OwlityAI generates relevant and comprehensive test cases based on application behavior and previous bug logs.
• Parallel execution: The tool runs tests simultaneously. On average, 95% faster, by the way.
• CI/CD integration: Jenkins, GitHub Actions, and other DevOps tools— you are not supposed to rack your brain figuring out how to use tools altogether. OwlityAI fits almost any option.

Bottom line

Looking for the next improvement within your company, consider all the benefits of autonomous testing. Software testing is often missing in terms of business influence. Yet, its role is hard to overrate. 

With AI in QA, you can progress much faster by speeding up time-to-market. This step also improves software quality, the main purpose of QA, as autonomous testing tools cover more test cases and identify defects earlier in the SDLC. 

Additionally, you don’t need to opt for something you don’t know specifically. Book a demo to learn how the novelty will suit you. Particularly OwlityAI.