Machine Learning (ML) is an important branch of Artificial Intelligence (AI) that enables programs or computers to conduct tasks without explicit programming. This involves forecasting future outcomes, scrutinizing diverse data sets and teaching systems so that patterns can be recognized. From a testing standpoint, the machine learning platform can detect bugs and errors, automate repetitive tasks, enhance testing accuracy etc. Hence, implementing AI in quality assurance is considered to be a wise move. In this article, you will learn the role of machine learning in automated testing.
The strategic role of Machine Learning (ML) in automated testing:
Machine Learning is a subset of Artificial Intelligence (AI) and is considered to be an important branch of artificial intelligence. Computer algorithms use data for learning. ML techniques and algorithms are used, so that, various aspects of the test automated platform can be improved. Defect prediction, test case prioritization, test data management, test case execution and test case generation are the varied aspects of automated testing.
Test automation tools help in a drastic reduction of manual testing. But, if there are software updates or changes, then consistent monitoring is required. Through the tactical use of ML models, automated tests can be able to work more effectively with minimal errors, thereby helping businesses achieve optimal results.
Companies are understanding and implementing AI in quality assurance to not only come up with a high quality product but also, streamline and scale the testing process.
Following are a few key factors that need to be considered while implementing ML in test automation:
1. Leveraging unit tests: The efficiency of the testing process can be improved by using machine learning for unit tests and thus the resources and time required for test case maintenance, generation and execution can be reduced. An example would be where a neural network is used to generate test inputs through which a variety of scenarios are covered.
2. The value of Robotic Process Automation (RPA): Regression testing can be automated by using RPA platform. Time-consuming and repetitive tasks such as test case execution and data entry can be automated. Eventually, the resources and time required for regression testing can be reduced.
An example would be where a suite of test cases are re-run through automation, after a new software has been released. The latest version of the application can be pulled automatically through configuration by the RPA software. The application’s latest version is pulled from a source code repository, then it is being deployed to a test environment and finally a predefined set of test cases are executed.
3. The significance of API: Traffic and API operations can be recorded using test automation tools through machine learning algorithms. Through this, tests can be evaluated and developed thoroughly. An example would be where anomaly detection algorithm can be used, so that unusual patterns can be identified in the API traffic.
When an ML model is trained on API traffic’s historical data, the standard traffic patterns can be learned by the model and thus any new patterns can be flagged that may deviate from the norm
- Automated visual testing (UI): Some of the problematic UI aspects can not be noticed by the human eye. Hence, UI issues can be identified and assessed by using ML picture recognition feature.
Following are a few key benefits of implementing ML in test automation:
1. Enhanced test coverage: Those areas of the system that contain bugs are easily identified by the ML platform. This in turn enables the testers to work on these specific areas and thus increase test coverage. Quality information can be easily extracted by organizations. Hence, applying the domain of AI in quality assurance is considered to be a viable move.
2. Test cases can be prioritized more effectively: Test cases can be prioritized by the ML platform, based on the likelihood of bugs that are present. Those companies that have adopted the ML platform said they can decide and work on crucial issues more effectively.
Conclusion: Automation testing can be taken to the next level by the apt use of machine learning platform. The team is always having an option to connect with a professional software testing services company that can provide tactical solutions to successfully implement ML in test automation.
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