Regression Test Suite Study Using Classic Statistical Methods and Machine Learning

Abstract

This work is interdisciplinary in nature. This work tries to apply latest discoveries in Artificial Intel-ligence to classic testing methodologies. Machine Learning which is the field of Artificial Intelligence is explored in this work. The work demonstrates that provided the test team maintains the required data, Machine Learning Algorithms can aid in deciphering patterns from the test data. Patterns of interest are the relation between testers experience in the project and bugs uncovered, relations between the testers experience and the efficiency of test case with respect to code coverage and test execution time. Relation between testers experience and efficiency of test case with respect to code coverage and execution time, relation between testers experience and bugs uncovered are explored using classic statistical techniques and clustering Machine Learning Algorithms. This clustering can be of immense help in test selection, prioritization, pruning and Regression test execution time reduction.