There was a time when businesses grappled with the extensive challenges of ensuring software quality through manual testing processes. The emergence of AI has completely transformed this landscape, automating the creation of test cases and data generation, which previously consumed substantial time and resources.
By harnessing AI in quality assurance, organizations can now focus on innovation and efficiency, as these intelligent systems streamline testing workflows, improve accuracy, and reduce the likelihood of human error.
AI tools are adept at detecting patterns and anomalies that might elude human testers and can simulate a wide range of user behaviors to provide comprehensive insights into software performance. This technological advancement allows companies to fast-track development cycles, enhance software reliability, and ultimately deliver superior products that meet customer expectations more effectively.
In this article, we are going to look into the role of AI quality assurance, the benefits of AI-enabled quality solutions, and the popular AI-automated quality assurance tools. So, without further ado, let’s get started!
Evolution of Quality Management Methods
The Quality Assurance strategies have changed in today’s time. The ones that existed back in the 1980s have crossed several strides to keep up with the changing requirements of the software development and delivery approaches and cycle.
The present case, popularized as continuous testing, is dominated by the Agile and CI/CD approach. Even after being denoted as one of the most developed stages of software testing using artificial intelligence, continuous testing brings some key challenges in Quality Assurance workflow processes:
Siloed automation
Absence of end to end requirements visibility
The high volume of test
To solve these issues, the industry must move to autonomous testing which ensures a zero-touch QA.
Role of AI in Quality Assurance: Test Cases for Autonomous QA
Software testing is inherently time-consuming, involving the meticulous development of a test suite and the generation of relevant test data. Manual testing is susceptible to human errors, leading to increased costs and extended timelines—an undesirable scenario for any company. Additionally, as software grows, the number of tests multiplies, complicating the maintenance of a comprehensive test suite and achieving optimal code coverage.
Leveraging AI can revolutionize your business by addressing these challenges and expediting the testing process. AI can automatically generate a test suite with the necessary test data, thoroughly crawl the software, and analyze outputs to detect errors and bugs that traditional functional tests might overlook.
Moreover, as AI agents can learn and develop themselves throughout the testing process, they evolve after changes in the codebase and find new application functions without human intervention.
Further, the QA teams can make use of AI testing tools for supplementing normal testing efforts, while getting the best test coverage in expedited time and with great accuracy.
The role of AI in automated quality assurance can also be seen in the testing tools where the tests are enhanced with AI-powered visual verifications, which, in turn, give out a range of different outcomes.
If we explicitly talk about the test cases of AI in automated quality assurance, here are the ways testers are using AI presently –
Implementing AI through image-based testing
Determining whether to run a test script
Using AI spidering Monitoring API testing
Automating tasks
With the active participation of machine learning and AI quality control, the time to come will be extremely experimentation-driven for the QA specialists.
Now that we have seen the role of AI in software testing, let’s look into the benefits of AI in quality assurance.
Read: How AI is Transforming Test Automation - 10 Key Use Cases
Benefits of AI in QA Testing
AI-powered testing tools, such as Katalon, replicate human behavior, enabling a transition from traditional manual testing to automated, precise continuous testing. These advancements streamline the testing process and enhance accuracy, reliability, and efficiency.
Here are some key benefits of integrating AI into QA and testing:
Expedited Timelines
There are many ways in which a team of developers speed up the app and software development process. Incorporating disruptions in the testing process is one of them. In place of going through thousands of lines of codes, AI is able to sort through the log files, scan the codes, and detect errors within seconds. Additionally, AI lacks the burnout syndrome and thus yields better and more accurate results. Also, artificial intelligence QA can evolve with the code changes. It can adapt and identify new functions and can be programmed to decide if something is a new feature or a bug arising out of code change.
Well Researched Build Release
By using AI in QA, it becomes possible for AI development companies to examine similar apps and software to determine what contributed to their success in the market. Upon understanding the market requirements, new test cases can be made for ensuring that the app or software doesn’t break when it comes to achieving specific goals.
Effortless Test Planning
Quality Assurance experts invest a substantial amount of time in planning test case scenarios, a process that needs to be repeated with every new version release. AI-powered QA automation tools streamline this process by analyzing the application, crawling through every screen, and autonomously generating and executing test case scenarios. This not only enhances efficiency but also significantly reduces the planning time required, allowing testers to focus on more critical aspects of quality assurance.
Expanded Role of a Tester
With artificial intelligence QA entering the picture, the teams of QA testers find themselves learning new skills. They need to up their skills in neuro-linguistic programming, business intelligence, math optimization, and algorithmic analysis.
Additionally, these new competencies allow QA teams to better understand and leverage AI tools, enabling them to create more sophisticated testing frameworks. This shift not only improves the accuracy and efficiency of testing processes but also empowers QA professionals to contribute more strategically to product development, ensuring that AI-driven insights are effectively integrated into the overall quality assurance strategy.
Predictive Analysis
AI automation in quality assurance can analyze and examine existing customers’ data to determine how users’ necessities and browsing practices advance. This permits testers, designers, and developers to stay ahead of evolving user standards and offer better support quality. With predictive analytics, the platform leveraging AI improves by analyzing user behavior and providing increasingly accurate forecasts.
Enhanced Writing of Test Cases
AI improves the quality of your test cases for automation testing. The technology offers real test cases that are quick to operate and easy to regulate. The traditional method does not allow the companies to analyze additional possibilities for test cases. With the help of generative AI in testing for quality assurance, project data analysis happens in a few seconds, enabling developers to identify new opportunities for test cases.
Improved Regression Testing
With the acceleration of deployment cycles, the necessity for comprehensive regression testing becomes more pronounced, often beyond the capabilities of manual efforts. AI can be effectively utilized to handle these repetitive regression testing tasks, with machine learning (ML) generating the required test content.
For instances of user interface (UI) changes, AI and ML can be employed to detect modifications in color, shape, or size. This automated approach replaces manual testing, ensuring that changes are accurately identified and validated, thus mitigating the risk of errors that may elude QA testers.
Visual User Interface Testing
AI helps in better user interface design and the visual approval of website pages. AI can test different contents on the UI. These tests are difficult to automate, typically needing human intervention for making a decision about design. Nonetheless, with ML-based visualization tools, contrasts in pictures are seen in a way that would not be feasible for people to pinpoint. AI in software quality assurance removes the manual effort of modernizing the Document Object Model (DOM), constructing a structure, and profiling risks.
Enhanced Defect Tracing
In conventional and manual testing, bugs and errors remain unnoticed for a long time and create hindrances in the future. AI in software testing can trace flaws spontaneously. As software grows, data increases, and thereby the number of bugs increases. AI in QA testing identifies these bugs quickly and automatically so that the software development team can operate smoothly. AI-based bug tracking perceives duplicate errors and identifies fingerprints of failures.
Continuous Integration and Delivery (CI/CD) Enhancement
AI-driven QA tools can seamlessly integrate into CI/CD pipelines, automating repetitive tasks such as code integration, testing, and deployment. This ensures that every code change is automatically tested and validated, reducing the time and effort required for manual interventions. By streamlining these processes, AI facilitates faster, more reliable releases, enabling businesses to focus on innovation and quality improvements.
Improved Test Coverage
AI for quality assurance can significantly enhance test coverage by automatically generating comprehensive test cases based on the analysis of the application’s behavior and usage patterns. This ensures that even edge cases and rarely used functionalities are tested, leading to more robust and reliable software products. By covering a wider array of scenarios, AI helps in identifying potential issues early, thereby improving the overall quality and user experience of the software.
Efficient Bug Detection and Application Verification
AI-driven tools excel at identifying bugs quickly and accurately. By leveraging machine learning algorithms, these tools can analyze large volumes of code and detect patterns that may indicate potential defects. This leads to faster identification and resolution of bugs, ensuring that applications are more stable and reliable. Additionally, AI can verify that all critical components of an application are tested, reducing the risk of missing important functionalities.
Resource Optimization
AI in quality assurance optimizes the allocation of resources by intelligently distributing testing tasks based on their priority, complexity, and required expertise. This ensures that the most critical tests are conducted first and that resources are not wasted on less important or redundant tasks. AI can also predict the necessary resources needed for future testing phases, allowing for better planning and utilization.
The Popular AI Testing Framework and Platforms
Quality assurance, though traditionally considered a gradual process, is increasing its prevalence with the help of AI-driven platforms. AI and quality assurance are revolutionizing the industry by enhancing efficiency, accuracy, and speed. Below are some of the top AI test automation tools and platforms available in the market today, addressing the critical question: how can AI tools improve quality assurance?
Test Automation Tools
Let’s explore the top-notch test automation tools that demonstrate exceptional efficiency and reliability. Discover how these tools can streamline your software testing processes and enhance overall productivity.
Selenium
Overview: Selenium is an open-source test automation tool widely used for automating web applications. It supports multiple browsers and operating systems.
Features: Cross-browser testing, parallel test execution, robust support for multiple programming languages (Java, Python, C#).
Use Cases: Businesses use Selenium for web application testing to ensure their websites work seamlessly across browsers and operating systems. It’s commonly used in regression testing to verify that recent changes haven’t broken existing functionality and in integration testing to confirm that various components of an application work together properly.
Appium
Overview: Appium is an open-source tool for automating native, mobile web, and hybrid applications on iOS and Android platforms.
Features: Supports multiple programming languages, cross-platform testing, seamless integration with CI/CD tools.
Use Cases: Companies use Appium for mobile app testing to ensure their applications run smoothly on both iOS and Android devices. It is ideal for cross-platform testing, where businesses can write tests for multiple platforms using the same API, thus saving time and effort. Functional testing is another key use, ensuring each application function operates in conformance with the requirement specification.
TestComplete
Overview: TestComplete is a commercial test automation tool for desktop, mobile, and web applications. It offers record and playback, script-based testing, and keyword-driven testing.
Features: GUI testing, robust object recognition, supports multiple scripting languages (JavaScript, Python, VBScript).
Use Cases: Many enterprises use TestComplete for functional and regression testing to ensure their applications perform correctly after updates or enhancements. Its GUI testing capabilities help businesses create robust automated tests for their user interfaces. Additionally, TestComplete is used in load testing to evaluate how applications perform under heavy traffic conditions.
Katalon Studio
Overview: Katalon Studio is an all-in-one test automation solution for web, API, mobile, and desktop applications. It provides a user-friendly interface and advanced capabilities.
Features: Cross-platform support, built-in templates, analytics and reporting, integration with popular CI/CD tools.
Use Cases: Organizations utilize Katalon Studio for API testing to verify their back-end services and ensure smooth integration between systems. Its functional testing capabilities help businesses ensure their applications meet specified requirements. Continuous testing is another major use, where Katalon Studio integrates with CI/CD pipelines to automate the testing process, allowing for faster delivery of high-quality software.
Ranorex
Overview: Ranorex is a comprehensive test automation tool for web, desktop, and mobile applications. It supports codeless test creation and offers advanced scripting capabilities.
Features: Robust object recognition, cross-browser testing, reusable test components.
Use Cases: Businesses use Ranorex for regression testing to ensure that new code changes do not adversely affect existing functionalities. Its GUI testing capabilities are essential for verifying that user interfaces behave as expected. Data-driven testing is another significant use, where Ranorex helps in running the same set of tests with different input values to verify how the application handles various data sets.
Top AI Platforms
Here are the top AI platforms empowering businesses to drive innovation and efficiency. Explore how these tools can transform your operations with cutting-edge AI capabilities.
TensorFlow
Overview: TensorFlow is an open-source machine learning framework developed by Google. It is widely used for building and deploying ML models.
Features: Extensive libraries, scalability, supports deep learning and neural networks.
Use Cases: Businesses leverage TensorFlow for image recognition tasks to identify and categorize images automatically. It is also used in natural language processing (NLP) to understand and generate human language, making it valuable for applications like chatbots and language translation. Predictive analytics is another common use, where companies utilize TensorFlow to forecast future trends based on historical data.
IBM Watson
Overview: IBM Watson is a suite of AI tools and applications designed to help businesses integrate AI into their workflows. By using AI in quality assurance, Watson enhances testing processes, ensuring higher accuracy and efficiency.
Features: Natural language processing, machine learning, visual recognition, AI-powered chatbots.
Use Cases: Organizations use IBM Watson for customer service automation, deploying AI-powered chatbots to handle routine inquiries and provide instant support. In healthcare analytics, Watson helps analyze large volumes of medical data to improve diagnosis and treatment plans. It is also employed in fraud detection, where its machine-learning capabilities identify and flag suspicious activities.
Microsoft Azure AI
Overview: Microsoft Azure AI offers a comprehensive set of AI services and tools for building and deploying intelligent applications. Using AI quality assurance, Azure enhances testing accuracy and efficiency, helping businesses maintain high software quality.
Features: Machine learning, cognitive services, AI-powered bots, robust cloud infrastructure.
Use Cases: Businesses utilize Microsoft Azure AI for predictive maintenance, analyzing data from equipment to predict failures before they occur, thus reducing downtime. Anomaly detection is another key application, where Azure AI helps identify unusual patterns that may indicate potential issues. Automated customer support is also a significant use, with AI-powered bots providing efficient and accurate responses to customer queries.
Amazon SageMaker
Overview: Amazon SageMaker is a fully managed service that provides tools to build, train, and deploy machine learning models at scale.
Features: Integrated Jupyter notebooks, automated model tuning, scalable infrastructure.
Use Cases: Companies use Amazon SageMaker for various applications, including forecasting demand, optimizing inventory, and enhancing supply chain management. SageMaker also aids in fraud detection and recommendation engines. Additionally, businesses leverage AI quality assurance through SageMaker to improve testing accuracy and efficiency.
Google Cloud AI Platform
Overview: Google Cloud AI Platform provides a suite of machine learning tools and services for developing and deploying AI models.
Features: AutoML, pre-trained models, scalable infrastructure, and integration with other Google Cloud services.
Use Cases: Enterprises utilize Google Cloud AI Platform for image and video analysis, automatically tagging and categorizing visual content. Sentiment analysis is another key application where businesses analyze customer feedback to gauge sentiment and improve their services. Speech recognition is also widely used, enabling applications to convert spoken language into text for various purposes, such as transcription and voice commands.
These tools and platforms can significantly enhance the efficiency and effectiveness of test automation and AI initiatives, enabling businesses to deliver high-quality software and intelligent applications that meet their users’ needs.
The Six Levels of AI QA Testing
Businesses nowadays are putting more effort into testing and maintaining their software product’s quality. This is why you must learn the following six stages of AI for quality assurance.
Level Zero
At this stage, writing the code is a repetitive process. Hence, adding a field to the page would mean adding a test. In a clearer manner, adding any form to a page means adding a test that checks all the fields. And adding a page means looking at all the components and forms through a whole new test. The more the tests, the greater are the instances of you failing to ensure the app’s functionalities as a whole. To solve this, you check all the failed tests to know whether something is a bug or a fresh baseline.
Level One
At this level, the better the AI is applied to your application, the more autonomous your QA will become. AI should not just look at the page’s Document Object Model but also its visual picture. Once the testing framework sees the page holistically, it will help you write checks that you otherwise had to write manually.
Today’s AI technology can help you write the test code by writing the checks. Also, they can check whether a test passes. If it fails, it will notify you so that you can check if the failure is real or happened because of a change in the software.
Level Two
Through level 1, the QA specialist would avoid the time-taking aspect of writing checks., You can also use the AI for testing the visual elements of the page. But checking every test failure – is a tedious task.
At this level, AI understands the differences in terms that the app users would also be able to understand. Thus, it will be able to group the changes from a number of pages, as it understands them semantically.
At Level 2, AI can tell the tester when the changes made are the same and ask whether it should accept or reject the changes as a group.
Level Three
In the previous level, human intervention is still needed for vetting any change or failure detected in the app. At level 3, AI does the job.
For example, by applying machine learning techniques, AI can examine visual elements of the app and decide if the UI is off, on the basis of the standard rule of design.
The AI at this level can evaluate pages without human intervention, simply by understanding the data and design rules. It looks at hundreds of results and analyze how things change over time. Then, with the help of machine learning, it is able to identify differences in changes.
Level Four
Until now, humans have still been driving the tests. Level 4 is where AI would take over.
Since level 4 AI is able to examine an app or a software semantically and understand it like a human would, it can drive the tests as well. This AI will be able to see the user interactions over time and visualize the interaction, understand the page and user flow.
Once AI understands the page type, it will use reinforcement learning techniques to start driving tests, automatically.
Level Five
This part is out of a science novel right now. At this stage, AI will be able to communicate with the product manager, understand the app, and drive the tests – all by itself.
While presently, AI is still on level 1, there are some automation events that are already using AI such as visual UI testing, API testing, automated quality assurance and testing, and Spidering.
Transforming the Future of Quality Assurance with AI
MarketsandMarkets projects that the overall AI market will grow from $214.6 billion in 2024 to $1,339.1 billion by 2030, reflecting a CAGR of 35.7% .
The future of AI in quality assurance promises significant advancements. Self-learning AI models will constantly improve their ability to generate and execute test cases, leading to even greater automation. This will free up QA professionals to focus on higher-level tasks like strategic test planning. Additionally, AI can be integrated with cognitive testing tools to create a more user-centric approach.
AI’s natural language processing capabilities enable the creation of sophisticated test scenarios, simulating real-world user interactions with high precision. Leveraging AI for QA testing allows for the development of more accurate and comprehensive testing protocols.
As AI evolves, it will redefine QA standards, ensuring flawless software performance and enhancing user satisfaction across diverse applications. Moreover, the integration of AI tools for quality assurance streamlines the testing process, offering unparalleled efficiency and reliability.
Source: This post was first published on appinventiv.com/blog/ai-in-quality-assurance