Software applications must be usable on various devices, platforms, and browsers.
Testing is often conducted during development to ensure that applications work smoothly with minimal bugs or errors.
Most QA professionals use cross-platform testing and cross-browser testing to check the working capabilities. However, machine learning is now shaping the software testing process.
This process can either be automatic or manual. In manual testing, humans perform the tests step by step without using test scripts. In contrast, automatic testing involves executing test automation frameworks and automated testing tools.
Organizations that use manual testing often face challenges with achieving sufficient coverage, while those who automate their testing process easily achieve excellent test coverage.
That’s because batch execution is possible with automated testing. Compared to manual testing, which requires human participation and might cause errors and bugs, automated testing reduces all the risks and shortens the time spent on the testing process. As a result, you will save more time when using this testing method and increase the process’s efficiency.
There are four broad types of testing, all of which are automatable:
- Differential testing: comparing application versions and builds, analyzing and classifying the differences, and learning from feedback.
- Visual testing: Leveraging image-based learning to ensure a UI looks the same to all users, analyzing how software looks across different operating systems and devices.
- Declarative testing: Involves specifying the intent of the test, and having an AI system figure out how to carry out the specified test.
- Self-healing automation: Auto-detecting and correcting changes to a UI.
Using machine learning in the testing process makes the testing smarter and more reliable. Here are the various benefits of machine learning in the software testing process.
Table of Contents
1: Improved Accuracy
The functionality of every software application revolves around the quality of tests carried out during the development process. Machine learning has redefined this process, accelerating the testing process. As more features and functions are added to the software, the complexity of the test process itself becomes more complex.
That means more code needs to be tested, which can overwhelm quality assurance engineers. ML techniques evolve with changes in disparate codes and adapt to the new functions. So, whenever the bots detect any new modification, they can automatically determine whether it’s a new feature or a bug.
This has improved the accuracy and time of the testing process, enabling humans to work on software improvement and optimization rather than testing.
2: Limits Test Maintenance
After new iterations of a software application are launched, there is a renewed need to test the overall performance, remove bugs, correct problems, etc. Traditionally, this also necessitates changing and maintaining test cases, i.e., test case maintenance.
Machine learning tests are automated and learn what’s changed across the site or app, effectively eliminating the need to develop and modify test cases. This is also called “self-healing”, as the ML test algorithm heals itself when you modify the software.
For example, if you move a “sign up” button, then the AI will simply learn where the button is placed – and you won’t need to do anything. AI will also sense if a component has been removed accidentally, feeding back reports to the devs.
Organizations can continuously carry out maintenance tests in each development cycle without much supervision. Before launching the software, ML testing ensures no bugs are hindering its performance.
3: ML Aids in Test Case Writing
A test case is simply a set of actions executed to verify particular features of the software application.
A well-written test case contains test steps, precondition, test data, and post conditions. This means that the testing process will be carried out efficiently, which makes the software more in demand than the others in the market. However, writing large volumes of test cases is often time-consuming.
Machine learning capabilities have helped eliminate the need for manual test case writing. Here, the developer needs to point only at the particular point to be tested. The ML technologies will then learn the application’s features and automatically collect usage data used to produce the expected test cases.
Here, the information collected is compared with the current structure patterns of the software. If there is any issue with the test cases, they will be highlighted as potential problems. Machine learning, therefore, enables dynamic and scalable test case selection that evolves as the software develops.
4: Reduces UI-Based Testing
Testing the user interface of software or applications is essential to ensure a smooth user experience.
In this case, the developers attempt everything possible to ensure that the interface is simple and meets the customers’ demands. User Interface testing validates whether the computer programs contain all the expected features and how user-friendly they are during the software development process.
Several machine learning tools have been built to reduce the complexity of UI-based testing, including Selenium which replicates the real experience of a user interacting with the AI. Selenium and similar tools automate everything from scrolling to clicks and UI navigation, highlighting potential errors and bottlenecks.
You can run an automated testing process that doesn’t require UI. Non-functional tests like security, the performance of UI, and unit integration have been made possible through machine learning. Still, you can monitor system logs to prevent bugs.
5: ML Helps in API Testing
APIs are computing interfaces that facilitate communication and sharing of data in separate software systems. Therefore, testing APIs is extremely important to ensure their integrability and compatibility.
Creating API test cases is time-consuming – machine learning enables testers to design cases at scale, covering all potential use cases. API tests can also leverage mined data, which ensures test cases are as close to reality as possible.
API test algorithms efficiently analyze millions of production API calls, redirecting engineers towards reporting and approving tests rather than building them. There are many ML-powered API testing suites out there, like IBM’s Cloud Pak.
Machine learning helps to interact with the API features and understand their functionalities. This way, it’s easier to detect bugs and generate a quick report for action.
Summary: What Are the Benefits of Machine Learning in Software Testing Processes?
Machine learning scales the testing process for software and APIs, enabling engineers to check and report bugs and errors rather than creating test cases.
Test environments such as Selenium automated elements of the testing process, enabling “self-healing” type UI testing that automatically adapts to UI changes.
In the near future, testing will likely become completely automated, eradicating the need for creating long-winded test procedures and reducing the time spent maintaining tests.
What is Software Testing?
Software testing is the process of analyzing software for bugs, glitches and UI/UX issues. This involves testing the software using scripted test cases. Software testing is tedious but necessary to create smooth user experiences across multiple devices and operating systems.
What is Automated Software Testing?
Automated software testing involves running scripts and test cases automatically, often with the help of AI and ML tools that engineer cases at scale. Automated UI testing automatically tests user interfaces by clicking, dragging, scrolling, etc.
What is UI Testing?
UI testing involves testing user interfaces (UIs) for bugs, glitches and UX issues. This is made simpler by automated UI testing environments like Selenium.
