International Business Machines Corporation
ANALYZING TEST RESULT FAILURES USING ARTIFICIAL INTELLIGENCE MODELS

Abstract:

A computer-implemented method, system and computer program product for analyzing test result failures using artificial intelligence models. A first machine learning model is trained to differentiate between a bug failure and a test failure within the test failures based on the failure attributes and historical failures. The failure type for each failed test in test failure groups is then determined using the first machine learning model. The failed tests in the test failure groups are then clustered into a set of clusters according to the failure attributes and the determined failure type for each failed test. A root cause failure for each cluster is identified based on the set of clusters and the failure attributes. The root cause of an unclassified failure is predicted using a second machine learning model trained to predict a root cause of the unclassified failure based on identifying the root cause failure for each cluster.