Abstract: Testcase recommendations are generated for a testcase creator application by training a learning function using metadata of previously generated testcases by parsing the metadata into steptasks, and providing the parsed metadata to the learning function to enable the learning function to determine relationships between the steptasks of the previously generated testcases, and using, by the testcase creator application, the trained learning function to obtain a predicted subsequent steptask for a given type of testcase to be generated. Each steptask describes one of the steps of the testcase using a concatenation of a step number of the one of the steps of the testcase, a module and a submodule to be used to perform of the one of the steps of the testcase, and a function to be performed at the one of the steps of the testcase.

Abstract: A method of processing test execution logs to determine error location and source includes creating a set of training examples based on previously processed test execution logs, clustering the training examples into a set of clusters using an unsupervised learning process, and using training examples of each cluster to train a respective supervised learning process to label data where each generated cluster is used as a class/label to identify the type of errors in the test execution log. The labeled data is then processed by supervised learning processes, specifically a classification algorithm. Once the classification model is built it is used to predict the type of the errors in future/unseen test execution logs. In some embodiments, the unsupervised learning process is a density-based spatial clustering of applications with noise clustering application, and the supervised learning processes are random forest deep neural networks.

Abstract: A method of processing test execution logs to determine error location and source includes creating a set of training examples based on previously processed test execution logs, clustering the training examples into a set of clusters using an unsupervised learning process, and using training examples of each cluster to train a respective supervised learning process to label data where each generated cluster is used as a class/label to identify the type of errors in the test execution log. The labeled data is then processed by supervised learning processes, specifically a classification algorithm. Once the classification model is built it is used to predict the type of the errors in future/unseen test execution logs. In some embodiments, the unsupervised learning process is a density-based spatial clustering of applications with noise clustering application, and the supervised learning processes are random forest deep neural networks.

Abstract: Testcase recommendations are generated for a testcase creator application by training a learning function using metadata of previously generated testcases by parsing the metadata into steptasks, and providing the parsed metadata to the learning function to enable the learning function to determine relationships between the steptasks of the previously generated testcases, and using, by the testcase creator application, the trained learning function to obtain a predicted subsequent steptask for a given type of testcase to be generated. Each steptask describes one of the steps of the testcase using a concatenation of a step number of the one of the steps of the testcase, a module and a submodule to be used to perform of the one of the steps of the testcase, and a function to be performed at the one of the steps of the testcase.