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Evaluating the Potential and Promise of Machine Learning for UI Testing

Evaluating the Potential and Promise of Machine Learning for UI Testing

As the qualitative focus is getting more intense with new and granular level parameters, user interface testing for the vast majority of apps is now trying every new methodology and means to ensure optimum output. The artificial intelligence and Machine Learning in recent times have come as the two most promising technologies to allow automation in the field of UI testing without really compromising on the output. While Machine Learning for mobile apps will continue to prosper as a groundbreaking technology, it is in the context of user interface testing that we can see the highest impact.

What Machine Learning as a new technology promises for unit testing professionals is really groundbreaking in many respects. Thanks to this technology, the testing professionals now can write unit test programs based on new test cases learned from the user inputs in interacting with the machines.

Let us have a look at some of the key benefits of Machine Learning test automation for mobile apps.

  • Writing new and unique test cases without human intervention or manual labor.
  • Thanks to Machine Learning the unique fuzzy matching can be applied to delegate testing programs for its deserving cases on the fly.
  • Machine learning will help the testing programs to be more adaptive without requiring human engagement in most of the times.
  • There are already several automation testing tools in the market and these tools will be further equipped with the incorporation of Machine Learning inputs. Many testing services in the time to come will find Machine Learning as something irreplaceable.

The Challenge of Automated UI Testing

Automated UI Testing

While Automation UI testing is getting popular to avoid human errors and making things faster, there are many challenges to automating the testing processes. The biggest challenge is, of course, knowing precisely the ways human beings will interact with an interface. As human interactions with all computing devices including mobile tend to be tremendously idiosyncratic and peculiar, addressing this with an automation testing mechanism is a big challenge.

When we think about addressing such deviations or idiosyncrasies with a testing program we have few credible options. Recording the gestures of the user during a test is a credible way to learn about the differences and peculiarities of the user interactions. But this approach is also not full-proof and assuring. There are both chances of success and failure in equal measures. Selenium test automation tool just does the same thing but when there is any new changes or modifications with an app update, the tool becomes clueless.

Actually, in the context of user interactions with an app UI, something continues to changes every now and then and it is hard to keep track of the same all the time. When a QA engineer after any modification opens the Selenium script and try to include a new interaction path, it may take months to complete the process. This is why even the most equipped automation tool cannot always ensure optimum testing output all the time.

ML for UI Testing Automation

Recently, Machine Learning (ML) algorithms have emerged as the most reliable solutions to pave the way for robust testing automation. Most ML-based testing algorithms now use cutting-edge computer vision inputs to know the visual rendering of the pages. This particularly helps in automation testing of apps that frequently need modifications to be able to run on different devices. The approach is further helped by scalable containers or cloud-hosted virtual machines where a UI for thousands of users can be carried out simultaneously.

ML Algorithms for Automated Testing

There are many proven methods and algorithms to use Machine Learning (ML) for automation testing of apps. For example, we have SVM which is nothing but a linear classifier kind of ML algorithm which is widely used by testing professionals for the purpose of binary testing tasks. We also have a ranking algorithm of various learning phases called MartiRank. This algorithm basically works by making several rounds of data collection for each iteration.

In this respect, it is important to know how we test to evaluate the changes in the behaviour of the software across different use-cases. Many of these test cases may not be used at all in real life while there can be a few that can be called out frequently. This data corresponding to different test cases helps the machine to write future test cases more applicable to address the specific user contexts. Such pristine testing mechanisms are actually helping in the development of automated mobile app testing tools such as HockeyApp and TestFlight.

Now coming to automation of GUI tests, we need to take a look at few deep learning modules that in the recent past have come to prominence. These deep learning modules have been tested on systems capable to track all the meaningful inputs such as clicks, texts and button push over the GUI interface. These interfaces gathering a considerable amount of user interaction data can be utilized on software to evaluate and track the user behavior in a meaningful way. With a lot of training data, the unit tests could be sharpened and more contextualized to particular users.

Future of automation testing based on Machine Learning algorithms

Machine Learning algorithms

Intel and Nvidia in the recent years made big investments with the objective of pushing Deep Learning and similar intelligent algorithms to deliver better and quicker results. This is slowly paving the way towards a world where dependence on AI and ML-based automation will shape the mobile apps of the future. As mobile apps are continuing to penetrate across all facets of everyday life, a number of test cases are coming up.


An increasing number of test cases coupled up with intense demand to address specific user peculiarities and behavioral attributes is going to make the field of mobile testing a complex and challenging one. Machine Learning technology has emerged as a silver lining of hope for the Quality Analyst Testers to beat this challenge and emerge victoriously.

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Written by Kapil Antala

Kapil Antala is a Design Lead at CMARIX InfoTech, a leading UI/UX Design Services Company with 12+ years experience. Kapil firmly believes that design is an art, which requires scientific execution to bring commercial results. He leads execution of Design Driven Engineering (DDE) at CMARIX for web, mobile and custom software development services.

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