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Machine learning in test automation

Machine Learning in Test Automation

folder_openTest Automation

Machine Learning in Test Automation

Test automation is the use of tools like Selenium to write scripts for software testing during SDLC. It diagnoses loopholes, assesses performance under practical situations, and ensures glitch-free application for business use to serve customer or clients or internal functions. The process of test automation requires constant and minute observation of incoming updates during software testing. It is time-consuming and prone to errors. Machine learning provides the perfect solution to tackle this situation during automation testing.

Machine learning along with AI makes the application testing truly automatic with minimum to nil scope of manual errors. In the following sections, we will explain what is machine learning? its relation with test automation, things to consider for ML in testing, the benefits of choosing algoQA, and more.

What is Machine Learning?

Machine learning is referred to as a branch of artificial intelligence and computer science. ML-based technologies imitate human ways to learn activities through data and algorithms. The process gradually minimizes human errors and improves the accuracy of operations.

ML based tools with algorithms and data makes classifications and predictions. It also offers an estimate of data patterns through analysis of labeled and unlabeled datasets.

Three categories of Machine Learning methods

  1. Supervised
  2. Unsupervised
  3. Semi-Supervised

How does Machine Learning relate to Test Automation?

Machine learning helps test automation in the following ways:

  • ML-based technologies automate the manual part of software testing.
  • It creates and updates automation tests automatically. Machine learning finds application errors and boosts the scope of the existing code.
  • It helps to produce better and more work in a comparatively shorter time.
  • QA team can place every change with every possible input.
  • These steps take significant time and money through automation test or manual software testing. Therefore, ML brings efficiency, saves time & money, and ensures glitch-free applications.
  • Machine learning helps developers to write test scripts and implement them. ML provides sustainable and extended test automation.
  • ML based tools observe and analyze trends, statistics, and consequences for organizations during the post-execution test evaluation phase. Reasons to Use Machine Learning in Test Automation
  • Test automation for mobile and web applications is often influenced by changeable aspects or updates from programmers. Therefore, you need machine learning for consistency.
  • You require machine learning to provide stability during modifications for the application or the test script.
  • Usually, non-ML automation test scripts are not stable. It is difficult to transform and adjust according to the required changes in the project. Non-static test scripts can cause errors, flaky checks, unreliable test results, development failures, etc.

How does Machine learning help development teams?

Organizations can leverage machine learning for the following requirements: Manage massive and diverse test data Organizations practicing an agile process for software development run multiple automation tests daily. These tests include solutions improvements, discovery, and more. It needs the collaboration of cross-functional teams and self-organizing skills.

Multiple tests and diverse teams usually lead to operational errors. Coordination without error is possible with machine learning technologies. Your team can easily divide and distribute data, observe patterns & trends, understand threats, and provide sustainable resolutions faster. Arrive at precise quality decisions Development teams can leverage the benefits of machine learning for the assessment of usability, application quality, and reliability features of the application. QA teams can write better codes faster with maturity by using machine learning. ML technologies scan new automation scripts, find out coverage gaps in tests, and analyze security problems.

Reliability of automation test Software engineers and testers often struggle to maintain updated automation test scripts with every now and then a new version sent for testing or new features released. Software testing with machine learning and self-healing abilities automatically recognizes changes made to a component locator. Things to Consider before applying machine learning for test automation Software testing and machine learning are reliable and efficient automation testing strategies. It saves time and boosts the productivity of the development team.

You must be thrilled to apply ML-based technologies to ensure error-free test automation for web and mobile applications. But before implementing, you must consider six crucial aspects for hassle-free use of ML in your organization.

Test Automation for User Interface A website or mobile application is a user interface for your organization. Manual testing is the usual method to test UI, but it leaves a scope for error. Eyes can miss some things on the page.

Machine learning works best for UI automation with the image recognition method. It verifies bugs, and the QA team can resolve the problem areas. Machine learning for unit automation tests Unit tests made with machine learning technology saves significant time for programmers. It allows them to focus on coding for the application. It also helps to maintain and author AI- powered unit tests in the advanced levels of the software development life cycle. API automation testing Machine learning with test automation records API functions and traffic. It helps the development team assess and make automation tests. Multiple Scripts of Automation Testing Machine learning-based tools can predict unproductive test cases and therefore saves money &  time. You do not have to test all the updates, upgrades, and code changes regarding the application. Machine learning methods for test data generation Test automation with machine learning provides data sets. These sets look like personal profile images and demographic details. Regression testing with robotic process automation Robots power automation and maintain them for web and mobile applications.

Benefits of choosing algoQA

  • algoQA provides multiple implicit and explicit use cases of machine learning. One of the implicit machine learning use cases is to bind locators with the name of elements.
  • One of the explicit machine learning use cases is to optimize the regression test suite.
  • algoQA provide forecast for every test case as to whether the test cases are likely to pass or fail.
  • algoQA is an AI-managed platform for end-to-end automation.
  • Single platform for API Test Automation, Load Automation, and UI-Based Functional Test Automation
  • Automation script for desktop based applications, web applications, mobile apps, hybrid applicationss, embedded software, and robotic process
  • Machine-generated thousands of easy-to-execute test scripts and test cases algoQA is AI-powered platform and provides machine learning support for accurate test automation. Learn more at and contact us for a demo today.

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