What is AI testing? A complete guide

AI comes off as a game-changer, reshaping how we see quality assurance. Take Google’s Android app testing process: They’ve implemented AI-driven fuzzing techniques that automatically generate millions of test inputs, uncovering bugs that traditional methods miss. As a result, a 100x increase in bug detection efficiency.

Traditional testing methods often can’t handle the speed and complexity required by modern applications. In modern environments, even traditional automated testing may not be sufficient.

Machine learning in testing aims to design, execute, and analyze tests with minimal human actions. Not an automation on steroids, but rather a fundamental shift in how we ensure software quality. AI can predict where bugs are likely to occur, generate test cases that humans might overlook, and adapt to changes in the codebase automatically.

Let’s dive deeper into AI testing. This guide poses a comparison of automated testing vs. AI testing and looks at real-world use cases.

Understanding AI testing

Artificial intelligence in software testing automates and enhances various elements of SDLC. The classic testing approach relies on manual scripting and predefined test cases.

On the contrary, AI-powered course uses smart algorithms to generate test cases and execute tests without extensive human participation. Analyzing the results is also a system’s responsibility. Yet, QA specialists should oversee the output since interpretation might differ from case to case.

Tech toolset: deeper overview

• Machine Learning algorithms: Supervised and unsupervised learning models, reinforcement learning.

• Natural Language Processing (NLP): Test case generation through ascertaining human language.

• Computer vision: Recognizing and interacting with visual elements in user interfaces.

• Neural networks: Deep learning models that identify complex patterns in data.

What AI testing can do:

• Automate test generation: To create test cases autonomously based on code analysis and user behavior patterns [real user data is required — with it, the output will live up to the appropriate level].

• Enhance test execution: Determine and prioritize high-risk areas, and then parallelize tests.

• Analyze results: Decode test outcomes, identify root causes, and suggest fixes.

• Adapt to changes: Continuously learn from new data and adjust testing strategies accordingly.

AI testing features:

• Predictive analytics: Historical data may tip the tool off on potential pitfalls and bottlenecks.

• Anomaly detection: Unusual patterns may be a sign of bugs or performance issues.

• Intelligent test coverage: System zeroes in on fail-like areas based on the previous analysis.

AI testing vs. traditional automated testing

Key components of AI testing

Machine Learning algorithms

The backbone of AI testing. They are trained on data such as code repositories, defect logs, and user interaction patterns to perform various tasks like:

• Pattern recognition: Identifying common failure points in the application.

• Predictive modeling: Anticipating where defects are most likely to occur.

• Decision making: Determining the most effective testing paths and methods.

How it works:

1. Training phase: Historical data allow models to train, analyzing past test cases and outcomes.

2. Validation phase: The model makes predictions and they go through additional validation, then compared to known results and assessed for accuracy.

3. Deployment phase: Once everything is fine, the model starts making real-time decisions in the real testing process.

Approaches [mentioned them earlier]:

• Supervised learning: Uses labeled data to make predictions and recognize patterns. Algorithms like decision trees and support vector machines.

• Unsupervised learning: Without overseen by humans. Clustering methods for anomaly detection.

• Reinforcement learning: Models that improve through feedback loops.

Google's STAMP (Static Test Analysis for Multiple Platforms) uses ML to predict which tests are likely to fail.

Natural Language Processing (NLP)

Through NLP AI-driven testing tools can ascertain human language and facilitate the creation of test cases from plain-language requirements.

How it works:

1. Text parsing: Lengthy and complex context is broken down into simple grammatical structures.

2. Semantic analysis: Decoding intent behind the formulations.

3. Intent recognition: Identifying what actions need to be tested based on the requirements.

Approaches:

• Language models: BERT, GPT-series models for understanding context.

• Syntax trees: Representing the grammatical structure of sentences.

• Entity recognition: Identifying key components like variables, functions, and user actions.

IBM's Watson for QA generates test cases from user stories through NLP: 80%-ish coverage without manual scripting.

Self-healing tests

Designed to automatically adjust to changes in the application, they reduce the need for manual test maintenance.

• Dynamic element locators: If an element's attributes change (like an ID or class), the AI can still find it using other properties or historical data.

• Automatic updates: Test scripts modify themselves to adjust to changes in the UI or functionality.

• Error correction: The system fixes minor issues in test scripts without human stepping in.

How it works:

1. Attribute analysis: Monitoring multiple attributes of UI elements to find the best match.

2. Historical data scans: Founded on past information, the system predicts the everyday state of the application.

3. Fallback mechanisms: If primary methods fail, there are always alternative strategies.

The benefits of AI testing

Artificial intelligence in testing is already changing the way we build the software development process. And it is not limited to expanded test coverage — it goes beyond. How exactly?

1. Increased efficiency

The system automates all time-consuming tasks and, thus, accelerates testing lifecycle:

• Immediate test case creation: ML algorithms can quickly generate extensive test cases, considering various scenarios without manual arbitration.

• Faster test execution: AI can simultaneously run thousands of tests with drastically decreased execution time.

• Automated maintenance: Self-healing capabilities allow tests to adapt to changes in the application automatically.

For instance, DiFF (Differential Fuzzing Framework) generates millions of test inputs per second. The framework foresees even edge cases that would take human testers years to embrace. The efficiency: Last year alone, it uncovered 150+ vulnerabilities in widely used open-source projects.

Google’s test execution pipeline is powered by TensorFlow-based models, an open-source ML “marketplace”. The system assesses the test’s criticality and historical performance and based on that allocates compute resources. This smart dishout reduced an average test suite execution time from 40 minutes to 6 minutes for a typical product release cycle.

2. Reduced human error

AI testing minimizes mistakes due to fatigue or oversight:

• Consistent execution: Prepared tests do the same steps strictly every time without “losing focus”.

• Elimination of repetitive tasks: The AI system handles routine and, this way, allows testers to focus on more complex issues that require human insight.

• Accurate defect detection: Advanced algorithms can detect anomalies that not every tester can spot.

AI also excels at visual regression testing. It uses convolutional neural networks trained on millions of UI screenshots to detect subtle visual anomalies across different browsers and devices, achieving 99.99% accuracy. How about comparing this with human capabilities?

3. Continuous improvement

New data-driven testing solutions continually evolve by learning from new information.

• Adaptive testing strategies: ML models adjust the way of creating new tests taking into account previous outcomes.

• Real-time feedback: On-the-spot insights into test results help QA teams make rational decisions much faster compared to manual and traditional automated testing flows.

• Environmental adaptation: The system adapts to changes in the application and testing environment.

One more perk is that OwlityAI also adapts to microservices architecture. When a service contract changes, the AI automatically updates dependent test cases across our entire ecosystem. This way, you get consistent test coverage without constantly double-checking this aspect by human testers.

4. Scientific-based development comes before engineering-based development

In this industry, we count on the expertise of data scientists and big data engineers. This is quite logical and fair as they use proven approaches in building software.

Yet, let’s point out that a formalized AI software engineering process, completed with defined development methodologies and clear criteria for quality validation, isn't always in place. This is an area we need to address to ensure the robustness and reliability of AI technologies.

5. Cost savings

Resource optimization, reducing manual effort, and time conservation — all these lead to significant cost reductions.

• Lower labor costs: Automation reduces the need for extensive manual testing teams.

• Reduced time to market: Faster testing cycles mean products reach customers sooner.

• Minimized maintenance expenses: Self-healing tests decrease the costs associated with test upkeep.

The system predicts demand, preemptively spins up resources, and intelligently routes tests to minimize latency and maximize resource utilization.

Once again on testing impact analysis. For any code change, the system predicts which tests are most likely to fail — so you can run a subset of tests for faster feedback. In 8 out of 10 cases, you’ll be able to provide accurate test results by running less than 10% of your full test suite.

How AI testing works

Below is a detailed look at what is under the hook of Artificial Intelligence testing, at the core processes that enhance quality assurance.

Choosing a dataset and gathering own data

The ABCs of any AI system is high-quality data. Where to search for it?

• Application data: “Guts” of your app. Your core code that runs the application is also a source. Application logs — processes that show what’s happening in the app while it’s working. And system architecture documents that show the foundation of your app.

• Historical test cases: You probably ran tests before. Your previous tests and their results are a perfect source. Inputs, outputs, and outcomes help the AI understand what has been tested and what defects have been found.

• User interaction data: This shows how people really interact with your software: their moves through the app, what they click on, and general use patterns. This behavior analytics reports the AI about common user journeys and potential edge cases.

To be more specific, very popular sources are Google Dataset Search, GitHub (Awesome Public Datasets, in particular), and Kaggle. Also, don’t exclude government sources.

Three steps to prepare data:

1. Clean: Draw duplicates and irrelevant data to avoid instilling faulty information in the AI model.

2. Normalize: Level out data formats for consistency.

3. Extract: Identify key attributes and metrics that are most relevant to testing like error rates, response times, and user engagement metrics.

Training the AI model

After data processing, it’s time to train the AI models for testing flow. We have already mentioned typical Machine Learning techniques (supervised learning, unsupervised learning, reinforcement learning).

Now, let’s list advanced training approaches:

• Transfer learning: Taking pre-trained models from similar projects to accelerate learning.

• Federated learning: Training models across multiple projects with data privacy maintenance.

• Active learning: Identifying the most informative test cases for manual labeling to improve model accuracy.

The training process itself:

• Model selection: Assess your objectives, goals, and requirements. Choose the right algorithms (e.g., neural networks, decision trees, support vector machines) based on the long-term strategy.

• Training iterations: Run multiple training cycles to tune the model's accuracy.

• Validation: Test the model on a separate dataset to evaluate performance and prevent overfitting (when the model fits the dataset by almost 100% that levels out the purpose of using this model).

Pro tip: Combine multiple AI models to achieve higher accuracy and robustness in your predictions.

Test execution and analysis

The next step is test execution and real-time results analysis.

Automated test execution:

• Predictive test selection: Using ML to choose the most relevant tests for each code change.

• Adaptive parallelization: Dynamically adjusting test distribution based on available resources and test dependencies.

Result analysis:

• Anomaly detection: Identifying unusual patterns in test results that may indicate new bugs.

• Search for the roots: Using causal inference models to trace failures to specific code changes or environmental factors.

"Explainable AI" techniques provide developers with clear, actionable insights into why specific tests failed.

Integration with development tools:

• Integration pipelines: Integrating with Jenkins, GitLab CI/CD, etc. to automatically trigger tests on code commits.

• Collaboration platforms: Sending alerts and reports to Slack, Jira, or other tools.

Feedback and optimization

One of the most powerful aspects of AI testing is its ability to learn and improve over time using feedback. And we have several ways here.

1/ Learning from outcomes:

• Result analysis: AI evaluates which test cases were effective and which were not and adjusts future testing strategies accordingly.

• Error patterns: There will be some recurring defects; the model will learn to recognize and detect them earlier.

2/ Model advancement:

• Hyperparameter tuning: Testing outcomes trigger model adjustments to ensure proper performance.

• Continuous learning: New data from each test cycle fresh the model with the latest application changes.

3/Optimization techniques:

• A/B testing: Traditional comparable practice to determine which strategy yields better results.

• Automated debugging: Model suggests code changes or highlights specific lines of code that may cause defects.

4/ Meta-learning:

• Hyperparameter optimization: Adjusting the model’s parameters autonomously to achieve optimal performance.

• Architecture search: AI for AI. Leveraging prepared models to create new ones for testing.

Balance exploration (trying new testing strategies) and exploitation (using proven approaches). Sometimes it’s worth using multi-armed bandit algorithms to manage this trade-off.

Choosing the right AI testing tool

Benefits of AI testing are useless if you choose an inappropriate tool. Before stopping over a particular one, size up these thoughts.

Key features to look for

• Seamless integration: The tool should effortlessly integrate with your existing development environment, including IDEs, CI/CD pipelines, and version control systems like Git.

• Versatility and tailored options at the same time: It should handle different types of testing: unit, integration, system, and acceptance testing. As well as have the ability to customize testing parameters and algorithms.

• Immediate insights: Without real-time dashboards and reports it’s more difficult to promptly identify and address issues.

• Adaptive testing: The tool should automatically adjust to changes in the application without requiring manual updates to test scripts, reducing maintenance efforts.

• Growth possibilities: You will scale, and so should your tool. With a growing user base, the tool should remain effective.

• Data protection: Robust security features are the base. Without them, don’t even mention compliance with industry regulations.

Evaluating compatibility with existing workflows

The tool should integrate seamlessly with your continuous integration and deployment processes, automatically triggering tests with each code commit.

Jira, TestRail, and Zephyr — any project management platform you choose should hit it off with your testing activities. The same with methodologies: Agile, Scrum, DevOps, whatever.

Size up the following:

• API accessibility: Open APIs enable custom integrations and facilitate communication between tools.

• Multi-language support: Ensure the tool supports the programming languages and frameworks used in your applications, such as Java, Python, or .NET.

• User access control: Role-based permissions enhance security and collaboration by ensuring team members have appropriate access levels.

OwlityAI: one-stop-shop for AI testing

• ML-driven test generation: Our tool automatically generates test cases using ML algorithms. With a focus on high-risk areas, it optimizes test coverage.

• Real-time reporting and analytics: Intuitive dashboards are our bread and butter. And OwlityAI’s got you covered, providing immediate feedback.

• Seamless integration: We partnered with popular development environments and tools like Jenkins, GitLab, and Jira. Expect no disruption.

• Self-healing tests: Tests adapt to changes in applications autonomously.

• Scalability: Definitely not a one-size-fits-all solution. However, OwlityAI fits small teams and scales effortlessly as your application and user base grows.

• Security compliance: Implements strong security measures to protect data during testing, ensuring compliance with industry standards and regulations.

• User-friendly interface: Your team members with low levels of technical expertise won’t get in trouble.

Three additional OwlityAI perks

1. Expert support: Available 24/7/365 through email and popular messengers.

2. Flexible deployment options: Whether you prefer cloud-based solutions or on-premises installations, OwlityAI accommodates your infrastructure preferences.

3. Innovation: We keep OwlityAI at the forefront of AI testing technology as our team educates themselves in top-tier courses from OpenAI, Google, and Meta.

Bottom line

Artificial Intelligence in software testing reduces human error and improves over time without your oversight and extensive effort. Anyway, this technology morphs traditional testing methods, creating something absolutely new.

Pay special attention to the quality of data and its proper processing. Size up the reputation of the data source as most datasets are gathered by humans. Consider Google Dataset Search, Kaggle, and government institutions as sources. The same attention needs the right tool.

OwlityAI is among the leading AI-driven testing solutions. With advanced features like machine learning-driven test generation and real-time reporting, it integrates smoothly with your existing workflows and ensures smooth scaling.