Artificial Intelligence is growing really fast with the global market of AI expected to reach over $136 billion as of 2022. Along with its growth, AI brings great benefits to companies by allowing them to do things faster and with higher precision. This article will highlight the place of AI in Software Testing and how it can advance the process.
Testing has always been an essential part of software development to ensure that the application works properly. With the release rate of products gradually increasing, engineers are turning towards automation testing to work more efficiently and effectively. Automation testing helps completing repetitive tasks in a shorter amount of time and with more accuracy.
As testing moves more and more towards automation, AI will eventually become more involved in the process. This means “the machine” will be taking over the execution of test codes, when and how to run the tests, etc. The value of AI lies in the reduced involvement of the developer or tester in the most mundane tasks.
You may wonder, “Don’t test automation tools do this already?” Well, yes, but there are limitations. Test automation tools run tests for you and deliver results. But most don’t know which tests to run, so they run all of them or some predetermined set. With the help of AI, these tools could review the current state of test statuses, code coverage, recent code changes, and other metrics, and then decide which tests to run and run them for you.
AI in software testing requires minimal human input and can help take a load off the tester’s mind. Still, we have a great need for human intelligence in applying business logic, strategic thinking, creative ideas, and the like to help machines ‘learn’ and enhance themselves.
As mentioned earlier, implementing AI can save testers time by eliminating mundane, repetitive tasks. Not only that, the “machine” can learn from the past projects to generate test scripts for comparable cases. AI can also detect and drop the failed test cases so you don’t need to waste time going through them. The timespan of software testing is reduced, and so is the cost.
As an application grows, it can be difficult to maintain a large number of test scripts. Implementing AI tools can tackle this difficulty by extracting and maintaining the suitable test scripts based on the testing needs.
Moreover, AI can also reduce the cumbersomeness of test maintenance when the User Interface changes. This is especially useful for solutions that need to frequently adjust the UI to adapt to their users’ needs. Normally, even the smallest change can lead to test failure and disrupt the test process. AI-based technologies can automatically detect these changes and take appropriate steps to maintain the test scripts, with no human intervention.
The unnoticed bugs and errors during testing can lead to some test failures from time to time. As a result, the testing team has to spend hours to assess and fix them. These might cause a delay in the software delivery or hold up the release of the product.
Artificial intelligence can quickly detect the bugs and point out the trends in several test failures so that the team can accelerate the process.
image credit: Autify, Inc
Tricentis Tosca is an enterprise solution used for software application end-to-end automated testing. This solution has incredible capabilities such as Vision AI, model-based test automation, risk-based test optimization, and so on. It delivers reliable, self-healing, platform-agnostic UI automation by combining deep convolutional neural networks with smart heuristics. In conjunction with Tricentis Tosca, Vision AI can be used to automate even the most difficult-to-access UI systems.
Functionize is a cloud-based automated testing solution for functional and non-functional testing (load and performance testing). It has some outstanding features like creating tests quickly (without scripts), executing multiple tests in minutes, and carrying out in-depth analysis. This tool accelerates test generation and management by utilizing AI and ML techniques. Plain English and NLP are employed to generate a functioning test. Furthermore, this application can run hundreds of tests from all desktop and mobile browsers in a single minute.
Launchable uses pattern recognition to see how likely a test will fail. This information can help to cut through the testing suite and eliminate some clear redundancies. Also, you can group tests and, for instance, run only the most problematic ones before deploying a hotfix. Launchable’s most recognised client is BMW.
Avo has a dedicated tool for managing test data, and the functionality includes AI data generation as well. The solution claims to mimic real-world data at large scale with some data discovery on top. Some of Avo’s clients include Sony, PwC, Tech Mahindra.
Parasoft is an automated testing tool that may be used to automate tests at any level of the software development cycle, from code analysis through user interface testing. It is a test suite with several components, and it has recently integrated AI and ML into software test automation to assist enterprises in implementing static analysis. The AI solution from Parasoft may be used to discover faults early and assure compliance with a wide range of functional safety, security, and coding requirements. It lightens the load on teams and guarantees that critical design concerns are addressed first.
The software market’s tremendous expansion implies that more software will be created. However, in order for software to perform properly and reach the market as rapidly as feasible, automation and artificial intelligence in software testing are required. Implementing AI in testing has created a leverage for companies to develop and scale their solution much faster and with more confidence.
If you are pondering over the adoption of this technology in the product engineering functions, we are here to help. Fram^ is a top software development company providing a full spectrum of services for your web and mobile applications. Don’t hesitate to contact us if you have any concerns!
