A few decades ago, artificial intelligence was like a dream for everyone. But now, it is an important part of every human’s daily routine. Smartphones, alexa, Google maps, self-driving cars, Siri- these are just some of the examples of things influenced or driven by AI. Not only that, but AI has also impacted our software development industry. AI testing and test automation are perfect examples of this technology.
Earlier, manual testing was the norm for organizations dealing in software development. But it took several hours to run manual test cases, which was a time-consuming and exhausting process. Then automation testing changed that landscape completely. It enabled quality assurance engineers to test software with minimum effort, providing better results.
Test automation requires time and effort for writing test scripts. After that, you also have to maintain and update those scripts. So, even test automation is not enough for high-quality software testing in minimum time. But, by involving AI, you can expect better results.
Using AI in Software Testing
The rise of automation testing was supported by the adoption of agile methodologies in software development. It enabled development teams to deliver bug-free software in small batches. Automation testing along with DevOps enables teams to release a fail-safe product for cloud deployment via continuous integration and delivery pipeline.
If we talk about software testing, AI works along with machine learning, cognitive automation, natural language processing (NLP), reason, and analytics. Cognitive automation uses multiple technological aspects such as data mining, machine learning, semantic technology, and NLP. Robotic process automation is one such example of connecting links between AI and cognitive computing.
Let us see how artificial intelligence is impacting the traditional ways of software testing:
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Automating Writing Test Cases
The biggest benefit of AI in software testing is its ability to write test cases for software automatically. Back in the day, web crawlers and spiders helped us in finding 404 dead pages. Now, AI/ML tools have advanced far ahead to understand the different usage scenarios of the product under test. They automatically crawl the application and collect useful information like HTML pages, page loading time, and screenshots. After that, they use the collected data to train the ML model of the app’s expected pattern.
When the test cases are run, they compare the current state of the app with saved or known patterns. In case any error, slow run time, or visual difference gets detected, the system marks it as a potential issue. But it’s the job of QA engineers to validate the bug or issue.
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Less Dependency on UI-based Testing
Automation without a user interface is one of the biggest changes that AI/ML brought to automation testing. Unit integration testing, performance testing, vulnerability, and security testing are also no exception. QA engineers apply AI/ML on various application logs like production monitoring system logs and source code. In addition, it helps in self-healing, auto-scaling, bug prediction, and early notification capabilities in the overall software ecosystem. AI testing also reduces testing cost, time, scripting, and error.
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Visual Validation Automation
It is an image-based testing technique that uses automated visual validation tools. The category comprises machine learning-based visual validation tools that detect anomalies related to the user interface, which go unnoticed by human eyes. The primary aim here is to ensure that every UI element of the application has the right color, position, shape, and size. They should not be overlapping with each other. A single ML test can detect all these minor visual bugs without requiring any human assistance.
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Better Reliability
A common problem that testing engineers face is the failure in UFT or Selenium tests due to changes made in applications by developers. But now, AI can make the code more reliable and maintainable every time the developer makes changes. Artificial intelligence and machine learning tools analyze the changes made in application code and understand the relation between them. Then the self-healing scripts observe those changes, learn the pattern, and then identify the change at runtime. This in turn reduces the flakiness and fragility of test automation.
Conclusion
Artificial intelligence and machine learning are the biggest game-changers in the software development and testing industry. Businesses are slowly adopting an AI-based approach for software and mobile application development, testing, and management. To know how AI testing can optimize your software QA and testing process, contact QASource now.
