How is machine learning algorithms changing software testing?
A Machine Learning testing platform can recognize changed controls more efficiently than a human. With constant updates to the machine learning and deep learning algorithms, even the slightest changes can be observed.
Now you might be wondered how that’s possible. Here we present you in a concise way how testing uses different machine learning algorithms.
Different Machine learning algorithms in testing:
K-Means Clustering: Test case classification to enhance regression testing.
C4.5 ( Decision Tree Algorithms ): Refine Black-Box test specification and improve the category-partition specification
General Classification Methods (SVM and others): Software Fault Prediction
Support Vector Machine: Test case prioritization in system-level testing without code access. for black-box testing
Artificial Neural Network (NN): Planning and scheduling of testing activities
Genetic Algorithms: Test data generation.
There are different advantages to using Machine Learning in the testing industry. From improving accuracy to saving time and money, the list goes on.
There are also various AI-power Test automation tools such as Testim, TestCraft, Applitools, etc.
AI software testing industry will produce more accurate results and will shorten the software development lifecycle than traditional testing techniques.
We expect that there will be specialized software and hardware solutions that can run AI deep learning and other AI algorithms and techniques to achieve more accurate testing results in competitive timeframes.