Achieving Agility in Projects Through Hierarchical Divisive Clustering Algorithm

Software products cannot be delivered to the market without proper testing. Only with the help of Testing, accuracy and quality of the product improves. Test personnel cannot compromise on the quality of the product and cannot afford to miss any defects. As the product's functionality expands, so does the testcase suite, and executing all of them takes more time and work. In this discussion, we'll look at how to use a machine learning approach called Hierarchical Divisive Clustering to optimise the test suite. With this approach, all the testcases are being considered as a single cluster in the beginning and during every iteration they are separated based on the similarity. This would help execute unique testcases without compromising on the quality which would help during any regression or sanity testing.