Think about a typical development life cycle. There are so many stages that require constant testing. It is a critical step that removes any defects that would interfere with the final product.
Now, imagine how much time testers would take with manual tests. Not only would the processes be repetitive, menial, and tiresome. They will also be prone to human errors that result from fatigue or inattention.
Test automation has been a game-changer. There is higher efficiency and reliability in results. Software testers can also cover wider scenarios.
There is no need to rewrite test scripts every time. The test cases are replicable across multiple test scenarios. The data generated from the processes are also important for better decision-making.
And now, test automation gets better with artificial intelligence. There is even greater efficiency, speed, and reliability in the processes. Our article shares five ways to use AI in test automation. Let’s dive into it.
If you look at any automation testing tutorial, you may see the term “spidering”. It refers to the use of machine learning to automate the process of writing tests.
The AI tools go through the app. It then collects data, takes screenshots, and downloads HTML. While the process is ongoing, it allows the teams to build datasets.
They also train the ML models to identify the patterns you expect for the applications. The learning allows for the comparison of patterns to check for any deviations.
The deviations could arise due to changes in the UI. Human testers will get the chance to validate whether the issues flagged by the ML algorithm are valid. The teams save time by not having to author the tests.
Unit testing is the process of testing individual codes within a system. The testers decide on what they want the units to be. It could be a class, line of code, or method.
The idea is to get the smallest units, for better clarity of how the codes are performing. Software developers need to ensure that the code meets and behaves as per design requirements.
Unit testing does not occur in isolation. Rather, it is a continuous process within the developmental cycles. You can, therefore, imagine that unit tests can be tedious and time-consuming.
The teams can increase efficiency with AI-generated unit tests. They must develop relevant code for the AI to mirror. But, therein lies a major challenge.
Anything outside the specification can limit the ability of the AI to perform. The software testers must prepare for such scenarios if they want to ensure the reliability of results.
One constant in software testing is the need for continuous updates or changes in code. It is a major challenge for software testers, yet one they cannot escape.
Artificial intelligence allows for self-healing automation. What does this mean? Test automation aims at pointing out any anomalies. The developers get an alert and work to correct it.
The process entails writing relevant scripts or instructions to take care of the problem. Thus, human developers still have a very active role to play.
With self-healing, the automation identifies and corrects the anomaly immediately. It does not wait for the developer to give any input.
Self-testing systems stay up to date through periodic scans. Further, the developers get recommendations and notifications based on the results of the scans.
There is no need to schedule manual prompts when it is time to check the system. Machine learning allows for automatic adjustment to any changes in the system.
ML understands relationships between parts and how best to use them without human input.
Automation testing tends to focus on back-end codes. The developers are looking for information about the functionality of the application.
Testers do not get critical information on how codes may appear to users. In the past, the team would depend on manual tests to get relevant UI insights. Automating such tests was quite difficult.
But now, they have AI to help with visual automation testing. There is great accuracy and reliability with the results. Artificial intelligence interacts with the interface as an end-user would.
But, it goes a little deeper because of its advanced observation capabilities. It can uncover issues that a normal user would not identify.
These include things like colors, shape, layout, and element sizes. The teams must also ensure that there is no overlap in any of the UI elements.
Correcting such in good time ensures a better UI in the final products.
Software testers will tell you they spend a lot of time running tests. A change in the application, no matter how small, can require an entire test suite run.
The solutions may be in the tons of data they generate. Yet, few have the time to sift through the data to identify common patterns.
Here is where AI can play a big role. It can help cut down on the number of tests the teams need to run to check the impact of any code changes.
The idea is to keep the numbers to a minimum. It will save the company time and resources. AI can also flag risk areas in the application’s current test coverage.
The team can identify new or duplicate failures based on debug or system logs. They can then concentrate on the new failures, to determine the causes.
AI in test automation is sure to provide tons of functionalities to testers. We have looked at some scenarios in our article above.
Creating and updating unit tests will ensure individual components are working as they should. Self-testing and healing AI automation move the need for human input in correcting anomalies.
The teams can also save a lot of time and money. They do not have to run entire test suites every time there is a change in the code.Cutting down on repetitive test cycles results in higher efficiency and high-quality products.
It removes the errors that come with manual testing. Pattern learning technologies like ML provide an excellent tool to predict future trends. It uses tons of information to identify predictive patterns while alerting teams on any changes.
Indeed, the future of test automation can only get better with artificial intelligence.
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