AI can recognize patterns and images that help to detect visual errors in apps through visual testing. It ensures that all the visual elements are attractive and work properly.
Incorporating AI in software testing makes it easy to handle repetitive tasks for more accurate results. It helps to decrease the chances of errors thus improving the overall accuracy of the tests.
Improved Test Coverage
AI-based testing can increase test coverage. It can flawlessly check memories, data tables, file contents, and internal program states. AI helps to validate the working of the program and if it delivers effective test coverage.
Efforts, Money, And Time-Saving
When there is a situation of change in the source code, testers may need to repeat software tests. Running manual tests is time-consuming and needs a lot of effort. But AI-driven tests help to manage repetitive tasks making them easier, faster, and more efficient.
AI helps to identify bugs and errors at early stages. It reduces the defects making the product free from bugs and reliable for users.
AI-Driven Software Testing Approaches
In differential testing, testers classify the differences and compare application versions over each build.
Visual testing tests the look and feel of an application. It uses screen comparison and image-based learning to validate the visuals.
Declarative testing aims to identify the purpose of the test in domain-specific or natural language. The system is responsible for deciding how to execute the test.
Self-heading automation testing auto-corrects the element selection in tests when the UI changes.
Categories Of AI-Driven Software Testing Tools
Differential tools use AI and ML algorithms to identify threats and regression. These threats may include security vulnerabilities, code-related issues, and more. Testers use differential tools for unit test automation, code scanning, etc. The tools used under this category include:
- Google OSS-Fuzz
Visual AI Testing Tools
UI layers undergo constant changes over time because companies wish to provide improved UX. This brings in the need for visual testing tools. These tools adequately test all the modifications of the UI layers. The tools used under this category include:
- Percy By BrowserStack
Declarative tools improve test automation stability and productivity. These tools use ML and AI and have compelling abilities related to:
- Robotic Process Automation ( RPA)
- Natural Language Processing (NLP)
- Model-based Test Automation (MBTA)
- Autonomous Testing Methods (AT)
These methods aim to eliminate error-prone, monotonous, and repetitive tasks by smart automation. The tools used under this category include:
- UiPath Test Suite
Testers use self-healing tools to overcome problems of reliability, flakiness, and maintenance issues. These tools are established on a record and playback mechanism. Consequently, the main engine of ML exists in the self-healing of the recorded scripts. The tools used under this category include: