Artificial Intelligence
The most recent technology being used in all fields and sectors is artificial intelligence (AI). AI has been permitting significantly dominant outcomes as it continues to infiltrate fields that were previously only accessible to human expertise. Similarly, software testing is employing AI to streamline the automation testing process and deliver higher-quality results.
The use of artificial intelligence in software testing makes the overall testing process faster, clearer, simpler and more cost-effective. Developers can use AI to advance the testing process and deliver higher quality results to businesses thanks to the strategic platform provided by AI-based testing.
The continuous testing process has long been supported by test automation tools. However, it is now important to use more efficient tools and methods, such as QA testing and integrating AI into software, in order to create high-quality software and guarantee a positive customer experience.
What is AI-based Testing?
A software testing method known as AI-based testing employs AI and Machine Learning (ML) algorithms to efficiently test a software product. AI-based testing seeks to improve the efficacy and intelligence of the testing process. You can use logical thinking and problem-solving techniques to enhance the entire testing process by incorporating AI and ML. Furthermore, this testing methodology utilizes AI testing tools to execute tests designed and conducted without human assistance, employing data and algorithms.
What changes has AI made in software testing?
Over the past two decades, the paradigm of software testing has undergone tremendous change. The testing path has been quite encouraging, from manual testing to test automation, with selenium being one of the best test automation technologies. However, the field of software testing must develop novel and thoroughly researched testing approaches in the fast-paced IT environment of today. The advent of AI-based testing has emerged for this reason and has been showing great promise.
While ML allows computers to learn independently without human intervention, AI algorithms can perfectly replicate human intelligence.It’s interesting to note that AI and ML include the creation of specific and one-of-a-kind algorithms that can access data, learn from that data by identifying patterns to inform judgments, and apply these predictions to efficiently perform software testing.
Furthermore, businesses are hurrying to embrace tools that can utilize AI and ML algorithms for efficient software testing. Furthermore, it has been observed that businesses benefit greatly from AI-based testing because it enables faster and continuous testing, complete automation without human intervention, and a quicker return on investment (ROI).
Some of the benefits of AI in software testing
Visual validation –
AI has pattern recognition and image recognition capabilities that together help to detect visual bugs by performing visual testing on applications. It helps to make sure that all the visual elements are engaging and can function properly. AI can recognize dynamic UI controls irrespective of their size, shape and analyses them at a pixel level.
Improved accuracy –
The manual testing method is prone to human error, especially for repetitive tasks. Automation testing helps eliminate these human errors. But when artificial intelligence enters automated testing, repetitive tasks are performed more efficiently and results are recorded more accurately. Thus, artificial intelligence helps eliminate small errors and improves the overall accuracy of tests.
Better test coverage –
AI in testing helps increase test coverage because it can transparently check file contents, data tables, memory, and internal program status. It also helps determine whether the program is performing as expected and provides effective test coverage.
Saves time, money, and efforts –
Whenever changes are made to the source code, you need to repeat software testing. Manually, this consumes a lot of time and demands significant effort from the tester. AI-powered testing, on the other hand, handles repetitive tasks accurately, quickly, and efficiently.
Reduces defects –
Artificial intelligence in testing helps detect defects early and quickly, which ultimately reduces defects and makes the product error-free and reliable for end users.
4 main AI-based testing methods?
Differential Testing – In this type of testing, we classify differences and compare application versions of each build.
Visual Testing – Visual testing leverages image-based learning and screen comparison to test the interface of an application.
Declarative Testing – It aims to define the purpose of the test in natural or domain-specific language. The system decides how to perform the test
Self-healing Automation –The system automatically corrects item selections in tests when the user interface changes.
What are the four main categories of AI-based testing tools?
Differential tools
- Launchable: It is possible to launch an ML algorithm that predicts the failure probability of each test based on past runs and each time the source code changes during the test. This tool allows users to save test suites to run tests that are likely to fail first. This tool can be used to run some dynamic tests that are likely to fail, reducing the execution time of a long-running test suite to a few minutes.
- Google OSS-Fuzz: It is a fuzz testing tool that aims to make popular open source software more secure, stable, and reliable.This tool combines modern fusing techniques with distributed and scalable performance capabilities. The tool supports C/C, Rust, Go and Python code
Visual AI testing tools
- Application tools:
It is an AI-powered visual testing and monitoring platform. It’s called the Next Generation Test Automation Platform with Visual AI. The most important features are Applitools Eyes, which helps to increase test coverage and reduce maintenance. The ultra-fast network helps test across browsers and devices and speeds up functional and visual testing by 30 times. This Applitools platform integrates with all modern testing frameworks and works with many existing testing tools like Selenium, Appium, Cypress, etc. - Percy from BrowserStack:
It’s an all-in-one visual review platform that includes amazing features like pixel-by-pixel differences, responsive differences, and snapshot stabilization. This tool enables cross-browser rendering, fast rendering and has parallel capabilities. Percy helps Teams automate visual testing. This Browserstack tool is commonly used to capture screenshots and compare them to baselines and show visual changes. This increases visual coverage and helps Teams deploy code changes with confidence.
Declarative tools
- Tricentis:
The next generation AI-powered test automation tool enables Agile and DevOps teams to quickly achieve their test automation goals. This allows teams to go beyond continuous experimentation with AI. It helps automate end-to-end testing of software applications. This tool combines several aspects of software testing (test case design, test automation, test data design and generation, and analysis) to test GUIs and APIs from a business.
Self-healing tools
- Mable:
It is the leading intelligent test automation platform designed for CI/CD. Mabl crawls application screens and starts running default tests common to most applications. It also uses ML algorithms to improve test execution and defect detection. - Testing:
This tool uses AI and ML algorithms to fully automate testing. They use artificial intelligence to accelerate the creation, execution, and maintenance of tests.Testim includes self-service maintenance of ML-based automated tests. This results in the rapid creation of robust automated tests
For more article you can also read my blogs:- SoftwareTestingLeaders.com
For more story you can visit on medium :- What Is Codeless Test Automation Work ?
Link to :- https://bootcamp.uxdesign.cc/best-ai-tools-for-business-afd87f314e9a
