AI testing tools: What you need to know to stay ahead

Applications become more complex and release cycles shorten. The further we go, the more it seems like traditional testing methods are inadequate.

Gartner’s 2024 Technology Adoption Roadmap seconds that: in 2023-2024, 80+% of companies worldwide have adopted or plan to integrate AI into their testing processes within the next few years. This is a necessary evolution to meet the demands of modern development cycles.

And AI testing tools are not a small fish in this big pond. At the moment they act as crucial instruments in this new landscape. Among the main peculiarities of autonomous software testing is using Machine Learning algorithms to automate and enhance various aspects of the testing process. Generally, AI-powered testing solutions accelerate test execution, improve accuracy, and reduce manual effort to let QA teams focus on the strategic vision.

This article aims to equip you with essential knowledge about AI testing tools — what they are, why they're important, and how to implement them effectively to stay competitive in an ever-evolving industry.

Why AI testing tools matter in today’s software development

Contemporary apps are more complex than ever with their distributed architectures, microservices, and rapid release cycles. In this light, traditional testing falls short of the mark in this environment.

Scalability issues: Manual testing and conventional automated testing struggle to scale with the growing size and complexity. You add new features continuously, the number of test cases increases exponentially, and you may end up struggling to maintain thorough coverage.

Speed constraints: Release cycles have shortened. The 14th edition of Capgemini’s World Quality Report highlights that 58% of companies find it challenging to keep up with the pace of software changes. The unassuming hint to adopt something new.

Adaptability limitations: Traditional test scripts are often brittle. Minor changes in the application's UI or functionality can cause numerous test cases to fail. For its part, it requires extensive manual updates and consumes priceless time.

The high-level comparison

Forrester Research indicates that 72% of top-performing companies implement AI in their testing processes. It’s quite understandable: wise resource allocation, effective usage, and better software quality.

The role of AI in testing

When choosing the right AI testing tool, it’s crucial to clearly define the way you will use it:

• Automating routine tasks: AI handles mundane tasks: test case generation, execution, and result analysis.

• Improving accuracy: We are living in a time when machines already can detect things humans miss. And the earlier you uncover defects, the better as it helps save time and money in the post-release stage.

• Smarter decision-making: With a colossal amount of data, AI provides insights into the application's most vulnerable areas. By prioritizing high-risk components, teams can allocate resources more effectively and enhance overall software quality.

What it means in practice

Staying competitive. The most influential consequence. Correctly chosen AI testing tools save your money, time, and – which is probably the most important – attention. Your QA team can concentrate on the strategic vision of developing your app: new features and potential compliance required, core milestones, bug analysis, and preventive measures.

For this reason, automated testing with AI means immediate feedback on code changes. This is exactly the thing that helps developers fix issues promptly, reducing the cost and time associated with defect resolution.

Accelerated testing cycles mean new features and updates reach customers sooner. Deloitte calculated that companies that adopted only GenAI tools are saving up to 20 minutes per day per employee.

Further calculations are easy as pie: with median dev salary in the US and only 5% productivity improvement, with about 1.8M developers in the country, the savings can hit USD 12B annually. Imagine what special testing tools can do.

Enhanced accuracy and comprehensive test coverage reduce the number of defects reaching production, leading to better user experiences and increased customer satisfaction.

Key features of AI testing tools

Test generation after each code commit

Sometimes, it’s hard to predict how the application will behave in “real life”. New-age testing tools analyze the real behavior and historical data to generate and update test cases through Machine Learning algorithms. Here is how exactly.

• What they do now: Models examine the way users interact with the application and identify common paths and edge cases.

• What they did before: The system can recognize patterns where failures are likely to occur. It simply scrutinizes past data and defect logs and creates targeted test cases to address these areas.

• Machine Learning algorithms: Test scenarios are grouped in clusters, which helps in predicting potential failure points.

AI in software testing accelerates the entire process, reduces manual effort (and saves resources this way), and adapts to application changes.

Self-healing

It is a feature where the tool automatically adjusts test scripts to adapt changes in user behavior or codebase.

• Dynamic element identification: Instead of relying on fixed identifiers, AI tools use multiple attributes to locate UI elements, making tests resilient to changes like modified IDs or class names.

• Real-time adaptation: Developers updated the app, and AI, in turn, updates the test scripts on the fly. The users won’t notice intervention.

• Reduced downtime: This capability minimizes test failures caused by minor UI changes.

Roughly speaking, the financial sector could especially benefit from this feature since it’s strictly regulated. AI and ML can potentially save banks, wallets, and other financial companies time and a huge amount of money by ensuring continuous compliance.

Predictive analytics

The system forecasts potential defects, identifies high-risk areas, and prioritizes testing efforts based on data-driven insights.

• Forecasting failures: The tool analyzes code complexity and recent changes to predict where bugs are most likely to pop up. Historical and defect data are also valuable sources for analysis.

• Sizing up risks: Modern tools have a risk assessment system. It assigns particular scores to different components, which helps prioritize testing efforts in the most vulnerable areas.

• Resource optimization: Focused testing on high-risk areas leads to more efficient use of time and resources, increasing the likelihood of catching critical defects early.

Netflix uses AI-driven predictive analytics to personalize content recommendations. Over 80% of watched content is based on their recommendation model. They have hit a 90% retention rate score, which is significantly higher than Amazon’s service's 75%.

Natural Language Processing (NLP)

This feature converts even plain language requirements into executable test cases.

• Requirement parsing: Algorithms interpret user stories and specifications written in natural language, extracting actionable test scenarios.

• Automated test script generation: The extracted scenarios are transformed into test scripts, bridging the gap between business requirements and technical implementation.

• Improved collaboration: This capability allows non-technical stakeholders to contribute to test creation, enhancing communication between developers, testers, and business analysts.

Integration with CI/CD pipelines

A plan isn’t a plan if everything goes smoothly, right? Not exactly. Seamless integration with continuous integration and continuous deployment (CI/CD) pipelines could really be fluent.

• Automated test triggers: AI testing tools automatically initiate tests when new code is committed.

• Continuous feedback: Real-time test results are fed back into the development pipeline. Developers identify and resolve issues almost “online”.

• Scalability and flexibility: Every project is unique, and integration supports testing across different environments and configurations.

Benefits of using AI testing tools

Time-saving, effort reduction

Artificial intelligence tools significantly simplify the testing process in terms of the human effort required. The algorithms automate tasks so that the QA team doesn’t need to look away from strategic tasks. Here is how it comes to life.

• Automated test case creation: AI algorithms analyze source code, user interactions, and historical defect data to automatically generate test cases. Techniques like static code analysis and user behavior modeling allow the AI to create comprehensive test suites without manual scripting.

• Efficient test execution: Machine learning models prioritize and execute test cases based on risk assessments. By focusing on high-impact areas first, critical defects are identified earlier in the development cycle.

• Maintenance minimization: Self-healing capabilities enable AI tools to adjust to changes in the application's UI or APIs automatically. For example, if an element's identifier changes, the AI can locate it using alternative attributes or machine vision techniques, reducing the need for manual test script updates.

Forrester found that new testing tools and technologies in general can significantly lower the number of security and compliance issues.

Expanded test coverage

Machines can generate a noticeably broader range of test scenarios, including edge cases that might be missed by traditional methods. How exactly?

• Combinatorial testing: AI algorithms generate combinations of inputs and conditions to explore a wide array of scenarios, employing methods like pairwise testing to optimize coverage efficiently.

• Anomaly detection: As ML models analyze a vast amount of data and previous test results, they allow for identifying unusual patterns or outliers in data that could denote potential defects. A markable help for the QA team.

• Adaptive learning: AI tools learn not only from past experiences but also from each following test execution. Moreover, they “refine” themselves to include new test cases based on discovered defects or change user behavior, for example.

Real-time feedback

All we do is for our users. A quite logical step is to implement user feedback alongside the feedback from the QA team on system failures. You may agree that AI/ML algorithms can investigate more information than humans. Let machines do the hard work:

• Integration with development environments: Seamless integration with IDEs and CI/CD pipelines allows tests to run automatically with each code change.

• Intelligent analytics: AI tools report that a test has failed and analyze the failure to identify root causes in addition. These may be specific code commits or configuration issues — the system knows the ropes anyway.

• Proactive issue resolution: Real-time alerts enable teams to address defects promptly, reducing the time between defect introduction and resolution.

This immediate feedback loop revs the development process and reduces the cost of late-stage defect fixes.

Scalability for large, complex applications

Benefits of AI testing go beyond the current workload. They are a step ahead and are built to handle the demands of modern, complex applications and those we only expect:

• Parallel execution: Leveraging cloud computing resources, AI tools execute multiple tests concurrently across various environments and configurations.

• Dynamic resource allocation: AI algorithms optimize the use of computing resources by allocating them based on test priority and complexity. This is where cost-efficiency enters the chat.

• Support for microservices and APIs: AI testing tools can navigate the complexities of microservices architectures and automatically generate and execute tests for individual services and their interactions.

Let’s look at this with a practical example. A multinational banking institution faced challenges in scaling their testing efforts due to a rapidly expanding suite of applications and services. Whether choosing the right AI testing tool can really help them? It can.

> By increasing test coverage through continuously generating test cases with different user scenarios.

> By reducing testing time through parallel execution and efficient resource utilization.

> By improving defect detection rate through scrutinizing analytics and anomaly detection.

Challenges and considerations when adopting AI testing tools

Companies that undergo adopting AI testing tools may encounter some challenges.

1/ Team training: Nobody knows everything. QA professionals and developers need to sort out new AI technologies. Training sessions, workshops, or even bringing in experts specialized in AI and machine learning — any educational initiatives will move you forward.

2/ Workflow integration: Integrating AI tools into existing development environments and CI/CD pipelines can be complex.

3/ Change management: Probably the most important part. Resistance to change killed endless quality teams and companies. Shifting to AI-driven testing often requires a comprehensive communication plan with clear benefits and expected outcomes.

Pro tip: Start early. Once you decide to adopt new-age technology, establish a vision, outline your steps, and drum up support from other leadership.

Data quality and availability

Poor data can lead to inaccurate predictions and insufficient test coverage. AI models rely on data to learn and make predictions. Incomplete, outdated, or biased data imminently leads to ineffectiveness.

Top three sources of quality data:

1. Historical test data

2. User interaction data

3. Production monitoring data or synthetic one

Startups and big companies: What to consider

Startups: With limited historical data, you should focus on user interaction data collected from beta testing or early adopters. Provide free early access to the test group to collect valuable insights. This investment will help train the AI model.

Big companies: Have extensive historical and production data. The mentioned data sources will build a more robust AI model. So your main task is to ensure the quality of data. If you are using external data sources as well, have a plan for unexpected app behavior.

Money. Money. Money

Budgeting is the place where many fall. What to include in calculations (at least): licensing fees, training expenses, and ongoing maintenance. Balance these costs against the long-term return on investment (ROI).

• Licensing fees: Speaking from experience, modern AI testing tools come with significant upfront costs. Evaluating different pricing structures and selecting one that aligns with the organization's budget is your bread and butter.

• Training expenses: Already covered topic — how to use the new tools effectively. Remember about required workshops, online courses, or hiring consultants.

• Ongoing maintenance: Regular updates, support, and potential customization add to the total cost of ownership.

High-level cost calculations for startups

Beforehand investment: We can’t help but admit the initial costs may seem substantial for a startup. Yet, the gained perks can offset these expenses.

Long-term return:

• Reduced time to market: With AI, you’ll significantly step up testing cycles. The logic is simple: earlier product releases — faster revenue growth.

• Improved software quality: Higher quality products → increased customer satisfaction → retention → constant profit.

• Resource optimization: Automation allows team members to focus on strategic initiatives rather than repetitive tasks.

How to choose the right AI testing tool

Assess your needs

Obvious yet important step. Every endeavor begins with an evaluation of your requirements. Without alignment over your testing needs within your company, chances are your effort will go down the drain. Size up these aspects:

1. Types of applications developed: Web-based, mobile, desktop application, or a combination? The type affects the compatibility and features required from the testing tool.

2. Complexity of test cases: Evaluate the intricacy of your test scenarios. Complex applications with numerous integrations and edge cases may require more advanced AI capabilities.

3. Existing testing infrastructure: Review current tools, frameworks, and workflows to identify compatibility requirements and potential integration challenges.

Easier with frameworks

1/ The TEST framework (technology, environment, skills, tools)

Technology: Identify the technologies and platforms used (e.g., programming languages, databases, cloud services).

Environment: The same with the deployment environments (e.g., on-premises, cloud, hybrid). How do they impact testing needs?

Skills: Assess the technical expertise of your team, especially with AI and automation tools.

Tools: Inventory existing testing tools to determine what can be integrated or needs replacement.

2/ The PIE framework (process, integration, evolution):

Process: Identify bottlenecks and areas for improvement in the current processes.

Integration: Determine whether the tool must integrate with CI/CD pipelines, project management systems, and other software (spoiler: it must).

Evolution: Forecast your growth and, depending on it, outline your future needs. Make sure the tool can scale and adapt over time.

3/ Bonus framework: The McKinsey 7S

This framework helps you evaluate strategy, structure, systems, skills, style, staff, and shared values. These factors determine how an AI testing tool can fit into your existing testing infrastructure.

Let’s take the most unusual aspect from the list — values. At first glance, it has nothing to do with testing, but only at first glance.

When your team commits to excellence, they will thoroughly test each application aspect. This may be time-consuming, resource-draining, and not always effective.

What to assess generally

1/ Ease of integration: If the tool integrates with your existing development environment seamlessly, it “earns” one conditional point. Include version control systems and collaboration platforms into this “contest”.

2/ Support for various testing types:

> Functional testing: Unit, integration, and system testing — if the tool fails the foundation, chances are it will fail the rest.

> Performance testing: Further is more — check the tool on load, stress, and scalability testing.

> Security testing: Instrument should identify vulnerabilities and support compliance requirements. Especially, if you operate in strictly regulated niches like the financial field.

> Scalability: Expect increasing workloads? Make sure the tool’s got you covered.

3/ Vendor support and community:

> Support: Reliable customer service and technical support are crucial for troubleshooting and maximizing tool benefits.

> Community: An active user community can provide additional resources, plugins, and shared best practices.

> Customization and flexibility: The ability to tailor the tool to your specific workflows enhances its effectiveness.

4/ Cost-effectiveness: Calculate the total cost of ownership. Apart from initial investment and licensing fees, make sure to include maintenance costs and potential ROI from efficiency gains.

5/ User-friendly interface: No one wants to click thrice and mess with a complex interface. Simple things are simpler to adopt.

Pilot first

Before going all in with AI, start with a trial or pilot program. You will be able to evaluate the tool's suitability in real life.

Take into account

Request a demo or trial period: Many vendors offer limited-time trials or demos. Utilize this opportunity to explore the tool's features.

Choose the metrics based on your context:

• Performance improvements: Measure reductions in test execution time and increases in test coverage.

• Integration success: The tool is integrated with your existing systems already — are you satisfied with the process?

• User feedback: Ask around about the usability and effectiveness of your app. Ideally if you can ask your current users and ICP (ideal clients) representatives.

Monitor and document outcomes: Keep detailed records of the tool's performance against your predefined metrics.

Review and decide: Analyze the results to determine if the tool meets your needs and justifies the investment.

Bottom line

AI-powered testing solutions go beyond traditional approaches. They ensure genuine autonomous testing: test case generation, execution, and maintenance. Outlier perk of such tools is actually the ability to identify outliers in users or system behavior.

This way, no critical edge cases will go unnoticed. Another benefit is real-time feedback, which empowers teams to focus on strategic initiatives rather than getting bogged down by repetitive tasks.

If you:

• Often get bugs in the post-release stage

• Spend extra money on bug fixing and want to save this money instead

• Short in time and want to deliver your product faster to grab a bigger market share

OwlityAI is here to help, guaranteeing automated testing with AI with cutting-edge features at an affordable price: self-healing tests, seamless integration with existing workflows, smart test case generation, and others. Give it a try, if you are ready to change your testing game. Request a demo or just contact our team to see how we can help.