A machine learning-driven self healing technique can be used in test automation to prevent flaky tests and reduce test failures. It also saves time on code maintenance. Continuous testing is possible with the DevOps model if self-healing is enabled.
Agile methodologies allow applications to be built quickly and subject to frequent modifications during initial development. Continuous testing is a process that automates tests to give instant feedback about business risks. Continuous testing is essential in DevOps to increase quality, reduce costs and speed up releases.
Continuous testing is part of the CI/CD pipeline. This helps identify issues early in code development. This helps developers decide when and how to release changes. This article will discuss how continuous testing can elevate testing to the next level by incorporating self-healing capabilities. You can reduce the time spent on repetitive maintenance of code and use this time to do activities that add more value to your project such as exploratory testing, increasing automation coverage, and so forth. Market research firms predict that continuous testing will increase from $1.15 Billion to $2.41 Billion in 2023 due to its benefits. This is a compound annual growth rate close to 16%.
What are the key factors to continuous testing success?
Many organizations have moved to DevOps over the past decade. This model has three main purposes. Continuous integration, continuous deployment and continuous testing are all used to ensure code quality. Figure 2 shows some of the key factors that contribute to continuous testing’s success: stable automated end-of-end tests (Web/UI), which are automated tests that don’t fail randomly, greater automation coverage, and tests that cover every code change in an application.
Continuous testing should cover all levels of continuous testing, from Component to API and UI to ensure greater test coverage. There are several significant advantages to testing from the end-to-end view over other types of testing. These tests simulate how customers interact with your application under normal conditions, as opposed to tests that only touch a small portion of your system. This coverage allows us to ensure that third-party services we don’t control are compatible with our application.
Continuous Testing Challenges
Automated tests can be flaky and often fail when you move to continuous testing. This is one of the main challenges in moving to continuous testing. Continuous integration in the CI/CD pipeline is hindered by these unstable E2E automated testing. A recent study found that 74% of automated tests fail because of brittle locators in the web page. Three issues are responsible for flaky tests:
- Frequent code modifications are required for web/UI locators like ID, Name and XPath.
- Element rendering: The web page should wait for the element to render or sync up.
- Issues with automation code
Flaky tests are false positives. This is because the flaky test fails have no effect on functionality and work as expected. However, due to brittle locators, they fail. Flaky tests are not common. Google reports 16% of their tests are flaky.
It is worth discussing the perspective of a quality engineer on flaky tests. It is not encouraging from a quality engineer’s perspective as the tests fail frequently, even when there is a good framework. Flaky tests can make it a tedious task to fix automation code. This takes extra time and analysis. This is a major problem with most automation tools. It is common to use element locators in order to identify elements and perform actions within test automation code.
Self-healing in test automation is a way to solve major problems such as test maintenance. Automation scripts can break when a change to an object property (Name, ID. XPath. CSS). Manually updating the object repository and identifying changes takes a lot of time. This can increase testing effort.
Project teams can adopt the Shift-Left method in Agile Methodology. This will increase efficiency and speed up delivery. There are many commercial and open-source software tools that can automatically detect and fix these changes without the need for human intervention. They also use machine learning algorithms and other AI-driven algorithms. Many commercial tools, such as Functionize, Applitools and Testim, Testim, Test.ai and Appvance.ai. have codeless automation ideologies. Some tools also have self-healing abilities. Due to the fact that these tools are commercial frameworks, details about the algorithms and solutions provided by them are not often disclosed. Self-healing is also possible with open-source tools like TestProject or Healenium.
When the AI engine detects an object property has changed, and that tests may fail, it extracts the whole document object model (DOM), and all the object properties. It uses dynamic location strategies based upon the weighted scoring system to extract the updated property. This is based on machine learning algorithms. The new object properties are used to automate actions on the object at runtime. This dynamic location strategy allows test scripts to run smoothly without the user being aware of the changes.
Self-Healing Automation – Selecting the Right Tool For the Job
There are many test automation tools available in the test automation world. Each one has its advantages and disadvantages. This list includes key features and self-healing abilities.
- Integration of CI/CD: Examples include Jenkins, Azure DevOps and Github Actions.
- Integration of Test Case Management: Examples include Zephyr and Xray.
- Integration of Workflow and Collaboration: Examples include Jira, Slack and PagerDuty
- Cloud platform support
- Integration testing at API and UI levels
- Cross-browser testing
- Blackberry, iOS and Android Mobile Testing
- Parallel test execution
It is important to evaluate your current technology stack, test tools, cost of open-source and commercial tools, as well as the team’s automation skillset, ability to adopt test tools, policies for organization, and whether there are any existing frameworks. A great way to start is to hold brainstorming sessions and select tools, then use the innovation sprints to test them out to make recommendations. My experience has taught me that there are some key aspects to consider when evaluating tools.
- Analyze and understand the current tools and technology used to develop the application. If the application was built with.NET, it is possible to evaluate both open-source tools and commercial tools. You can also use commercial tools like Mabl and Functionize or open-source tools like TestProject. Evaluation can be done.
- Assess the current automation framework to determine if it could be expanded to use self-healing libraries or tools. If your existing automation framework is Java Selenium-based the team can assess and perform a proof-of-concept to extend the existing framework to use any self-healing support libraries like Healenium.
- Based on your organization’s tool policies, and the project budget for open-source and commercial tools, you can identify appropriate tools to meet your project testing needs. Consider tools like Katalon, TestProject, and Healenium if you allow open-source tools in your organization.
- Assess the team’s test automation skills and identify the tools that best suit their application testing needs. If the team is subject-matter experts in an application domain, but does not have the automation skills, then codeless tools like TestProject, Functionize and Tosca could be a good option.
Automated tests will continue to self-heal and bring value to agile teams during implementation. Self-healing techniques can be adopted in testing practices to reduce the number of tests that fail and prevent flaky tests. This will help you save time, effort, and money. This is an opportunity where teams can reap the significant benefits of the software testing machine learning-driven innovations.