Autonomous testing: Solving the biggest pain points in software QA

Have you ever tried to have two full-time jobs at the same time… in the same company? This is exactly what your testers might have. With project growth, test suites grow too, and maintaining turns into a second full-time job. 

Testing teams feel these pain points in software QA: instead of strategic testing, days are drained into repetitive maintenance. And there is no chance to spot deeper software risks until … the prod is down and your Product owner is tearing their hair out.

Let’s be more down-to-earth and skip pitches for QA process optimization. 

This article is about AI-powered QA solutions — your weapon against the root causes of common QA headaches. We’ll hash out how autonomous testing teases out these issues.

Read further to find out how to escape the constant firefighting and put your team’s expertise back where it belongs.

The biggest pain points in software QA

Slow testing cycles

Your time to market heavily depends on your testing speed. Even semi-automated scripts frequently lag behind sprint velocity, not to mention the manual approach:

• Delayed feedback: Developers finish coding, yet QA tests still aren’t complete. Feedback on code relevance, clarity, efficiency stalls.
• Increased costs: Each delay extends release timelines and increases labor and infrastructure expenses.

Example:

A mid-sized SaaS performs manual regression testing:

• 500 test cases, each taking 20 minutes on average.
• Total test execution time: roughly 166 hours per sprint (500 tests × 20 min / 60 min/hr).
• Let’s take an average tester rate of USD 45/hour; the cost per sprint is ~ USD 7,470.

Provided each sprint takes two weeks, that’s almost USD 15,000 a month spent on regression testing. If you are that with a deeeeep pocket, skip the calculations, but if not, size autonomous testing benefits up.

Limited test coverage

Even the best human testers are humans. They miss edge cases, they miss unusual scenarios, they get tired. So, it’s nothing groundbreaking in blind spots:

• Rare but critical user interactions often slip unnoticed.
• Gaps in coverage affect production, end users, and eventually your reputation.

Impact:

A missed critical bug = costly emergency fixes, downtime, and the mentioned reputational damage. Some sources state that post-release issues cost 100x of fixing in the design stage. At least, they are for sure 5-10 times more costly to resolve compared to pre-release identification.

High costs of manual testing

Continuing the money topic, let’s discuss scaling. Regarding manual testing, it quickly becomes unsustainable as your product grows:

• Each manual test requires significant human effort.
• Salaries, onboarding, training, and administration expenses soar.

Calculate:

Take a growing fitness app with:

• 200 regression test cases, each requiring 30 minutes to run manually.
• Total test execution per regression cycle: 100 hours (200 tests × 30 min / 60 min/hr).
• Provided 4 builds per month, that’s 400 hours.
• Average annual salary is USD 85,000 per tester, so you should lay down at least USD 7,000 monthly expenses.

AI-powered testing tools typically reduce these manual efforts by up to 80+%. Specifically in this example, OwlityAI drops the testing budget to around USD 1,300/month.

Resource bottlenecks in QA teams

The main problem from a business perspective is that your QA team doesn’t impact the company’s results as powerfully as it can due to routine tasks like maintenance. Namely:

• Frequent manual updates and flaky test triage drain team productivity.
• Constant firefighting means your best testers lose motivation and quit.

More practical:

QA engineers commonly spend 60–70% of their time on repetitive test maintenance tasks. Shifting this workload to AI-powered QA solutions frees up your senior testers to focus on exploratory testing, usability improvements, and overall product quality.

Difficulty adapting to rapid code changes

Take into account:

• Outdated test scripts impact the bottom line: Agile environments are too fast and have multiple changes daily, so existing scripts just have no chance to be effective. 
• Increased risk of losses: Broken or outdated tests create false positives or overlook real bugs.

Case: a microservices app releasing updates daily:

• Manually maintaining tests across multiple services typically requires at least two full-time QA engineers.
• At a USD 90,000 annual salary per tester, they spent USD 180,000 per year solely on keeping scripts up-to-date.

How autonomous testing solves these pain points

Accelerates testing cycles

AI changes the way we understand software testing: test execution is now faster, complex (even exhaustive), and more efficient, whether it is regression testing, performance, or load testing. 

Manual testing → semi-automated scripts → autonomous QA solution — leverage cloud infrastructure to run tests in parallel and achieve predictable results faster.

Clear benefits:

1. Shorter feedback loops. 

2. Autonomous run 

3. Instant validation of each code push. 

What it looks like in practice

When a developer pushes new code, OwlityAI:

Visually scans and analyzes UI changes.

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Generates relevant test scenarios.

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Executes parallel cloud tests with real-time results directly in Jenkins, GitLab, or Azure DevOps.

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Detects bugs and exports them into project management tools (Jira, Azure Boards)

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Alerts devs.

Eventually, hours or even days of manual testing turn into daily iterations, immediate fixes, and reliable releases.

Enhances test coverage

AI-powered testing tools have specialized algorithms trained to analyze application behavior, UI elements, and interaction patterns. The system covers edge cases, complex user flows, and uncommon scenarios, exactly thanks to deeper analysis and a huge amount of data. 

Clear benefits:

1. Exhaustive test coverage. 

2. Significantly reduced chance of missed bugs in production. 

3. Dynamic discovery of vulnerabilities and bugs hidden in rarely accessed parts of the application.

What it looks like in practice

Adaptats: AI continuously updates tests based on application changes.

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Analysis of the visual and behavioral parts: The tool captures subtle UI changes, unexpected behaviors, or newly introduced workflows.

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Generates predictive scenarios: OwlityAI identifies new app functionality or modifications, proactively generates tests to cover scenarios testers haven’t even anticipated.

Reduces testing costs

We can’t stand buzzwords. Yet, AI in software testing is a real game-changer, because it pushes the typical productivity level to a new level we couldn’t even imagine before. 

You can evaluate these improvements in monetary equivalent. Autonomous QA solutions automate repetitive tasks and replace more testers with an efficiently tuned autonomous workflow.

Detailed financial comparison

Given: A mid-sized SaaS company with a manual QA team.

• Five testers, each with an annual salary of USD 70,000; annual QA labor costs of USD 350,000.
• Implementing OwlityAI reduces repetitive manual tasks by at least 80%. Now, just one QA lead (USD 85,000/year) oversees strategic testing. 
• The annual subscription to OwlityAI (approx. USD 40,000/year depending on complexity and usage) drops total annual costs to USD 125,000, saving you USD 225,000 per year.

Handles growth without extra overhead

Scaling is another reason why you should think over the QA process optimization. The app scales, the number of test cases grows, but you needn’t add more infrastructure, testers, or manual test scripts. AI dynamically adjusts to growth.

Clear benefit:

1. Your team saves time and resources for strategic work rather than routine maintenance. 

2. The scalability becomes predictable, manageable, and cost-effective.

Fits agile and DevOps

DevOps and Agile are all about rapid code changes and evolving requirements. With OwlityAI and other next-gen testing solutions, you can rest assured test scripts are updated and the changes are covered.

What it looks like in practice

OwlityAI’s algorithms track changes and recognize modifications in the UI or functionality.

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The tool instantly adapts the tests to new code commits.

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OwlityAI learns from previous test results, refines the test coverage, and reduces false positives or flaky tests over time.

AI in software testing: A specific case

Given: A digital payments provider that recently faced soaring maintenance overheads, sluggish test execution, and frequent bugs despite regular regression testing.

Solution: To test OwlityAI, an autonomous QA solution.

The scheme: 

• The tool integrates via REST APIs directly into their GitLab CI/CD pipeline.
• After deployment to staging, the AI visually scanned the app UI elements and generated relevant test scenarios on its own.
• Test runs concurrently across five cloud threads and slash regression cycles from days to hours.
• The tool automatically caught unstable tests, recalibrating or updating test logic dynamically.

Outcomes in 6 months:

• Three times more releases per month.
• Defect rate dropped by 64% with no emergency fixes.
• Autonomous testing saved roughly USD 220,000 annually by trimming the manual QA workforce.

5 steps to get the ball rolling with autonomous testing

1. Identify your pain points

Assess your QA bottlenecks. Here’s how.

• Speed: Measure your current test execution duration compared to target release windows.
• Accuracy: Track defect leakage rate into production environments.
• Coverage: Review the ratio of test coverage across features and edge cases.
• Cost: Check monthly QA spending versus the overall budget.
• Team capacity: Assess workload distribution, determine the current testers’ focus and the desired focus.

Practical tip:

Create a simple spreadsheet/task in Jira to map each pain point to its severity (high/medium/low) and frequency (daily/weekly/monthly). This will always keep your focus on hot areas and provide measurable improvements.

2. Choose the right tool

Understanding your needs, just pick the relevant tool within your budget. Pay special attention to flexibility, CI/CD integration, and core QA automation features.

Why OwlityAI: The top 5 features

1. Autonomous scanning:

Computer vision automatically detects UI elements and features requiring testing.

2. Automated test scenario generation:

Comprehensive, robust test scenarios instantly cover your entire application, including obscure workflows.

3. AI-powered test prioritization:

AI ranks test cases by severity (High, Medium, Low), so your team addresses critical issues immediately.

4. Continuous maintenance:

Automatically identifies and updates outdated test cases as your UI evolves.

5. Parallel test execution in the cloud:

Runs tests simultaneously across multiple cloud-based threads.

3. Start with high-impact areas

Target the tasks currently draining most of your team’s time and resources.

→ Quick-win strategy: Automate regression tests first to free the team from repetitive tasks and have something cool to show to your stakeholders.

Hidden trick:

Prioritize automating regression cases directly related to your critical business workflows or revenue-generating features. Business values money.

4. Train your team

Effective training approach:

Break down training into several focused workshops (~2-3 hours) rather than overwhelming your team with extensive sessions.

Practical tip:

Empower champions within your QA team — seasoned testers who can coach peers and quickly demonstrate the benefits of autonomous QA solutions through practical examples.

5. Monitor and optimize

Key metrics to track:

• Defect density: Number of defects per thousand lines of code or per release. 
• Test execution time: How much time autonomous testing reduces compared to manual or semi-automated methods.
• Test coverage: Track growth in comprehensive test coverage.

Bottom line

It’s impossible to describe in detail all the pain points in software QA with only one article, if you don’t want to overwhelm your reader.

So, long story short, AI changes the way we test, and the autonomous testing benefits are available for businesses of all sizes.

So, why not start now? 

OwlityAI is the next-gen AI-powered testing tool that makes testing faster, easier to perform, and more efficient.