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In that case, the weighted value of an agent would be decremented and another value would be tried in its place. However, there may be situations where the value changes or it fails. These comparisons create variable values that we refer to as “agents.” When an agent is created, it is given a value and over time, that value is modified up or down based on the interactions as the program is being run.Īs an example, if a program has an assert statement where it finds the correct value, it then increments that agent variable (or leaves the value alone) based on it being successful. To get into the details of both AI and Machine Learning could take an entire book or 2 but, at its heart, these tools operate under the principle of making small tests and comparisons. Can AI and Machine Learning Help Us Heal Our Tests?Ĭurrently, there are efforts underway and tools available to leverage both AI and Machine Learning to help make scripts adaptable to change and to making our tests more robust.
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Both of these approaches have their issues. I am familiar with this and have tried a number of methods over the years to effectively deal with this, ranging from implicit waits, to creating loops to confirm an element is present before moving forward. Even if the elements on the page do not change, if the contents of the page are dynamic or are sufficiently complex in how they are rendered, the time it takes to render the page might prove to be an issue. A problem arises when we are not able to guarantee what those values are or there is a chance that those values might change. If these locators are consistent or do not change, then the test is likely to run and will be able to respond because the locator or element it is looking for or the assert it needs to determine a pass or fail condition is present. Often, when we think about Test Automation, we are looking at tools like WebDriver or other variations which allow the user to select actions and perform operations for specific elements on a web page or app using locators. These situations are frustrating and I often mutter to myself, “Can’t the test figure these things out on their own?” Today, the answer is “Yes, they can!” Or, at least they can to a point. However, what often happens is that a test changes slightly or the underlying environment changes in a way that may cause a test to fail at certain times but not others. And, of course, if there is a real error, the need to be fixed and rerun. When tests go bad, they cause failed builds, necessary research, and examination. In CI/CD environments, automated tests are the linchpin of a successful build and deployment.