Software testing is a tedious process that is long and repetitive when done without the correct tools or processes. Running regression and unit tests manually is an ineffective method that consumes time and valuable resources.
Streamlining software testing is a priority for AI/ML industry experts. They work around the clock to find effective solutions that can help testers and QA analysts to run tests more efficiently when compared to manual testing. An automation test approach reduces the time of spending on the software testing process and gives more reliable results compared to manual testing which requires human participation. Are they succeeding in this mission? Find out more about how machine learning is helping specialists simplify the software testing process through test automation tools and processes.
Table of Contents
Less Test Monitoring
Software testers need to constantly monitor tests being run automatically on their tools of choice. Unfortunately, this is also too much work, and the main reason why test monitoring is essential is due to updates being released by the testing software.
Testers have to ensure that all is good when the tool receives an update and make any necessary updates to the code scope. For example, if the testing software runs 24/7 and gets an update when there is no tester monitoring, the system could pause that test.
Only resuming the test on the following day wastes valuable time. Implementing machine learning can help make the necessary updates when someone is not monitoring the test. This reduces human resources or promotes a fair distribution thereof.
Autonomous Adjustments to Test Cases
If you add a new feature or codebase, regression tests must be run and reported to software testers. Regression tests are repetitive because they go through the same processes repeatedly to assess the state of the software project being developed.
The scope and scale of a software project increase when new features are added. As a result, the same regression tests might not be practical. In that case, the test scripts need to be adjusted to match the new project’s scope and scale.
These changes could be tedious but necessary to make the software testing more productive. Fortunately, machine learning reduces the legwork by aiding the test script adjustments autonomously. As a result, the tasks left up to software testers are relatively minor.
Higher Test Productivity
Manual testing takes a very long time unnecessarily, this has been established, but you can also optimize test automation even further with machine learning. Running tests autonomously is a considerable achievement for software development teams, but it can also be a little ineffective without AI/ML.
The volume and quality of software tests run autonomously without AI/ML tools can be improved. By using these smart tools, test scripts can be automatically updated to fit each test requirement.
At the same time, your test processes will keep up with the changing software testing landscape. If the software you use for test automation receives an update, machine learning will adjust the script codebases to suit the new test environment. As a result, software testers can increase their test quantities without compromising quality.
Machine Learning in UI Testing
UI testing is one of the few tests that QA analysts prefer not to run automatically. Test automation in this regard seems counterproductive because it might return inaccurate results. Therefore, testers go through each software page and check for any flaws and UI misbehavior.
The process is also tedious because all functionalities must be tested to monitor how the software behaves. With AI/ML, UI tests can be automated using visual recognition software.
The benefit of using machine learning-powered UI testing tools is that they can pick up even minor glitches, which could be hard to detect for testers. Since testers are trying to go through the software as quickly as possible, they might miss those minor glitches, but with machine learning, these errors get reported.
Minimizes Running Impractical Tests
As much as some tests are repetitive, the test standards might not remain the same all the time. Test data changes and the functionality priorities shift with each round of tests. Due to changing expected outcomes of the test, the scripts also have to change because they might not be effective anymore.
Running tests on ineffective scripts produces unproductive results that are not actionable. Therefore, software testers need to manually change the script with each new test being run with different objectives. This process is just as tedious as regression testing, but it has to be done.
Machine learning plays a crucial role in this by indicating whenever a test case might not be productive. In that way, software testers know when to change the test case scripts after being notified by machine learning tools.
Machine Learning-Powered Data Tests
Data-driven tests are an invaluable commodity because they produce the best results for software products. Unfortunately, conducting data tests based on predefined insights impedes the scope of the test. As a result, the QA analysts and testers will not have a clear picture of the capabilities and shortcomings of the app.
Machine learning addresses this issue by powering data sets. Adding machine learning to your test automation strategy will help you have fresh data to conduct tests on. In addition, AI/ML can create user personas that mimic what would happen if end-users used it in real life.
The data can autonomously update itself within the parameters provided and governed by data input principles. From that information, software testers can have the ultimate results depicting the best representation of that app’s performance and functionality.
Benefits of Machine Learning in Software Testing
The core benefit of ingraining machine learning in the software testing process is cost savings. Development teams can save a lot of money by adding this AI-powered element to their test automation strategy. Less personnel will be required to monitor the tests and configure the tests, which will reduce the human resources budget.
Alternatively, the skills of QA analysts can be put to good use in another role within the company. However, machine learning produces accurate results at a fraction of the cost while also streamlining the QA workflow.
When implemented right from the beginning during unit testing, AI/ML can significantly expedite the development-to-deployment pipeline. High-quality software products can be released much sooner than expected, which will increase the beta testing phase timeframe, improving overall results.