How Can the Advancements In Machine Learning Improve Test Automation?
Every major enterprise leverage testing nowadays to efficiently validate their application or software solution to ensure that it performs as expected in the real world under real conditions. Ultimately, this testing of an application helps the enterprise to identify software bugs, code defects, or dissatisfactory user experience (UX) before it’s in the hands of their actual customers. Here we get to know about Machine learning to improve Test Automation in detail.
While customers often mention bugs and defects synonymously, they are actually not quite the same. A bug typically refers to when the software solution performs something it wasn’t programmed to do. This unpredictable behavior may result in the application crashing or buffering which could significantly reduce customer satisfaction.
A defect, however, refers to when the software solution does not meet the initial expectations. Defects are frequently noticed in the user interface (UI) which will result in the application slowing down or becoming unresponsive to user interaction.
Therefore, to properly identify each and every bug, defect, and flaw in the software application, businesses adopt a better strategy, which is to automate as much of their testing as possible. For this, there are multiple testing frameworks out there to help you in any trending programming language, and also QA teams can leverage several continuous integration tools to help kick off complicated tests when building enterprise code.
The Need for Test Automation
As QA teams move up the testing hierarchy to various other testing methods such as functional tests, performance tests, integration tests, and various other complex tests, the process gets significantly more challenging to maintain. Therefore, most enterprises tend to automate these tests as manual testing would require more resources and test infrastructure.
Especially automating Functional UI testing is specifically imperative for modern applications, since there are several user elements, and it is the most fundamental strategy used in customer-client interaction. Moreover, manual testers might find it challenging to identify the front-end errors on multiple devices, platforms, and operating systems. Meanwhile, by automating the functional testing process, the code will itself test the user interface on several different platforms without the need for human intervention.
Testing an application obviously takes a considerable amount of time, infrastructure, expense, and resources as every additional feature has to be tested thoroughly and must be validated under multiple scenarios. With the help of automation, QA teams can not only test the new features but can also ensure that the additional code has not affected the previous code in any way.
For a large enterprise application, this process called Regression testing can take weeks to do manually which is why several testing teams put a real priority on trying to automate the complete testing process.
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How Can the Progressions In Machine Learning Improve Test Automation?
Quickly analyse excessively high quantities of test data
There are several enterprises that implement continuous testing within Agile and DevOps. Such enterprises will have to repeatedly perform an extensive variety of testing types on multiple sets of applications, several times a day. The testing includes:
- Unit testing
- API testing
- Functional testing
- Accessibility testing
- Integration testing
With each and every test execution, the results from these tests create a tremendous amount of data. Manual testers can find it extremely challenging to analyse these huge test data multiple times a day, which will not only prevent them from identifying and fixing key issues in the software but will also increase the application development life cycle.
With machine learning, testers will no longer have to manually analyse the test reports but can automate the tasks. This will help them quickly identify the issues in the product, provide solutions and also identify areas to focus on in the upcoming tests.
Hence with ML systems, QA teams and enterprises will be able to better analyse huge test data, understand trends and changing patterns, eliminate business risks, and most of all make decisions faster and deploy products on schedule.
Leverage self-healing to enhance test stability over time
In conventional test automation services, the QA teams frequently find it challenging to continuously sustain the scripts each time a renewed build is being provided for testing, or unique functionality is included in the application under test.
With traditional approaches, these circumstances sabotage the test scripts — either because of the new element ID that was presented or modified since the previous app or because a new platform-specific ability or popup was added that interrupts the test execution flow.
This could be exceptionally critical in the mobile landscape as new operating system versions are introduced every year and this typically alters the user interface and includes the latest notification alerts or security popups on top of the application. This type of spontaneous occurrence would normally break a standard test script.
But with the implementation of Machine Learning and self-healing capabilities, a test automation framework can seamlessly recognize the modification created on an element locator (ID) or a screen/flow that was included between predefined test automation steps.
It can not only identify it but the self-healing capabilities can also help in quickly fixing them on the fly, or notify and guide the developers with a quick-fix option.
For enterprises that are thinking of including machine learning within the DevOps pipeline and testing strategies, it is also crucial that they consider how ML can explore and scrutinize continuous CI builds, notify trends within the unit or API testing, and do other testing areas.
With ML algorithms, QA teams will be empowered to provide comprehensive test automation services and seamlessly identify builds that are always bugged, prolonged or ineffective. Nowadays, testing requires continuous human intervention and often tends to fail without proper attention.
With machine learning entering this process, the primary benefit is a shorter development cycle and more stable yet effective builds, which results in faster feedback and cost savings.
Author Bio Ricky Philip is an industry expert and a professional writer working at ThinkPalm Technologies. He works with a focus on understanding the implications of new technologies such as artificial intelligence, big data, SDN/NFV, cloud analytics, and Internet of Things (IoT) services.