The surface area for testing software has never been so broad. Today’s applications interact with other applications through APIs, leverage legacy systems, and grow in complexity from one day to the next in a nonlinear fashion.
Testing apps require a bigger budget for tools and resources compared to programming. Every organization today is hunting for the best possible talent. Earlier software developers used to write code and testers checked it for quality. This won’t suffice today. You write code that tests the application, and you’re happy because you can run the same tests repeatedly on every release. This allows you to concentrate on the most important aspect of testing — thinking.
But nobody’s helping you write that automation code. Writing the code itself is repetitive and adding any field to a form means adding a test. Adding any form to a page means adding a test that checks all the fields. And adding any page means checking all the components and forms on that page.
The more tests you have, the more they will fail when developers make a sweeping change to the application. So you’ve checked every failed test to verify whether you have a real bug or just a new baseline.
Releases that happened once a month now occur weekly, and updates are factored in almost every day. Thus, it is evident that the key to streamlining software testing and making it smarter is Artificial Intelligence (AI). AI is a broader concept and Machine Learning (ML) is its subset that allows machines to learn from data without being programmed explicitly.
AI in Software Testing? But Why?
AI in testing is less about HAL 9000 and more about statistics-based, machine-learning AI.
Machine Learning can consume tons of complex information, find predictable patterns, and then alert you about those differences. That’s why ML is so powerful.
Writing more reliable Automated Test Scripts using AI
How often do your tests fail due to developers making changes to your application, such as renaming a field ID? It happens to me all the time. Although Selenium is the most broadly used framework, it’s rigidly tied to the specific elements on the front end. But imagine if there is a tool that can use machine learning to automatically adjust to these changes. Wouldn’t it make tests more maintainable and reliable?
What if I say that we can solve this problem by using dynamic locators the AI extracts from the Document Object Model (DOM)? AI analyzes all the object trees and properties from the DOM and can create a list of different attributes for a particular element in real-time. When an attribute of an element changes, the AI goes to the next attribute in the list to locate the element and continues the process until the element is located. The tests are more stable, the authoring and execution of tests are much faster, and the tester has to spend less time on maintenance.
Automating Test Creation and Maintenance
MABL, founded by Dan Belcher and Izzy Azeri (former Google employees), allows users to create automated tests without scripting. It uses a machine learning algorithm to adapt the tests when the application changes. The service also finds bugs such as JavaScript errors, broken links, and slow page loading. This reduces the time and complexity of maintaining tests. The application has an AI to analyze the test results and identify regressions.
In the future, Artificial Intelligence will be able to observe users performing exploratory testing within the testing site, using the human brain to assess and identify the applications that are being tested. In turn, this will bring business users into testing, and customers will be able to automate test cases fully.
What Would Humans Do?
Oh.. now you are worried that artificial intelligence (AI) will soon replace all visual testing and QA teams. Don’t be. Autonomous testing tools are here to help, not replace you. Besides, testers and QA teams should be spending more time thinking about business value — i.e., thinking about how to test and ensure quality, rather than performing automation testing just as a means to an end.
Being able to train an ML algorithm requires you to come up with a testing model. This activity needs someone with domain knowledge. Many automation engineers are creating models to help with this development.
With this change, there is a need for folks who not only know how to automate but can also analyze and understand complex data structures, statistics, and algorithms.
Don’t Panic!
With MABL, AI in software testing is already here. Are you worried? Probably. Are you out of a job? Probably not.
The World Quality Report predicts the rise of new roles in QA and testing:
- AI QA Strategists – To understand how to apply AI to business. They will need to master both business and technical knowledge.
- Data Scientists – To sift through test data and use predictive analytics, mathematics, and statistics to build models. They must have a deep understanding and experience of data analysis techniques.
- AI Test Experts – To be involved in the testing of AI applications. In addition to traditional testing expertise, they will need to understand machine-learning algorithms and natural-language processing techniques and to have other highly technical skills.
So stop worrying and do what you do best: Keep learning, Keep automating.
This post was published under the Quality Assurance Community of Experts. Communities of Experts are specialized groups at Modus that consolidate knowledge, document standards, reduce delivery times for clients, and open up growth opportunities for team members.
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