Artificial Intelligence makes up for most of the manual work that goes into testing a system or a software. Moreover, it can speed up the process and reduce errors. These are some of the things that need to be kept in mind when it comes to AI.
Technical alternatives: There are several open source AI tools like TensorFlow, CAFFE, Microsoft Cognitive Toolkit, NLTK, and more.
AI needs proper recruitment: AI cannot be done by everyone, unless the concerned person has the right training. It is important to find someone who does it best.
AI is not the be all and the end all: It is an addition to greater computer power and not the be all and the end all.
AI needs to be fed with data: It is important to feed data into AI. One should collect and structure data with an eye for AI application. AI will be more useful if you have the proper data to train it.
AI is not the solution for all problems: First decide where you need AI, instead of just jumping into conclusions. It is easy to fall prey to the buzzword.
Testing tools for AI
Some of the most in-use testing tools for AI include:
First Wave: This comprises of old-fashioned vendor tools like WinRunner, Silk Test, QTP, and more. These are likely to set the stage for future testing automation innovations like Selenium.
Second Wave: The second wave began with Selenium, focussing more on developers and programming best practices while creating automated tests.
Third Wave: Some of the third wave automation tools include:
- SauceLabs: They were the first players in cloud-based test automation space. Having run over a million tests a day, they have a treasure trove of data that can be used to help their customers in a better way.
- Testim: It speeds up the authoring, execution and most importantly the maintenance of automated tests.
- Sealights: It is a cloud-based platform. Sealight was created since managers and engineers get super busy usinf CI and CD practices where they have frequent releases and not enough time to test the entire application several times.
- ReportPortal: This too has a machine learning algorithm that helps in analyzing the results automatically. These algorithms use all the data that have recently been recorded and you can be confident about the status of your test cases.
- Mabl: It is similar to Test.AI and runs functional tests against the app or website. In this tool, one can “train” the tests to interact with the applications. Once the recording is complete, your trained tests will run at a predetermined time and alert you.
- Applitools: There are no visual processing settings, percentages or configurations that need to be set up to create visual testing with Applitools. The algorithm is adaptive.
- AI: It is said to have added AI brain to Selenium and Appium. It does not require any coding and does not require messing with element identifiers.
- ReTest: It tests your system automatically. ReTest claims to be different from other test automation tools because it was built specifically with testers in mind.