公开(公告)号：US11568173B2

公开(公告)日：2023-01-31

申请号：US16892347

申请日：2020-06-04

Applicant: EMC IP HOLDING COMPANY LLC

Inventor： Malak Alshawabkeh ,  Motasem Awwad ,  Samer Badran ,  Swapnil Chaudhari

IPC: G06F11/00 ,  G06F9/44 ,  G06K9/62 ,  G06F11/36 ,  G06N3/04 ,  G06F40/30 ,  G06N3/08

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.

公开(公告)号：US20210383170A1

公开(公告)日：2021-12-09

申请号：US16892347

申请日：2020-06-04

Applicant: EMC IP HOLDING COMPANY LLC

Inventor： Malak Alshawabkeh ,  Motasem Awwad ,  Samer Badran ,  Swapnil Chaudhari

IPC: G06K9/62 ,  G06N3/04 ,  G06F40/30 ,  G06F11/36

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.