公开(公告)号：US10089661B1

公开(公告)日：2018-10-02

申请号：US15380664

申请日：2016-12-15

Applicant: Amazon Technologies, Inc.

Inventor： Srikar Appalaraju ,  Vipul Bhargava ,  Amol Wanjari

IPC: G06F9/44 ,  G06Q30/02 ,  G06F17/30

Abstract: Techniques are disclosed herein for identifying software products, available from an electronic marketplace, to be tested. Data associated with software products is accessed and analyzed to determine what software products to test. The data analyzed may include, but is not limited to, download data, crash data, ratings data, marketplace data, usage data, and the like. A machine learning mechanism may be used to predict a popularity of a software product, classify the application into a category relating to whether a potential anomaly is identified for the software product, and determine whether to test the software product. A score may also be calculated for the software products that indicates whether or not to test the software product. The predicted popularity, the classification and/or the score may be used to determine whether to perform further analysis or testing with regard to a software product. For instance, the score may be used to determine that the software product is to be tested by a testing service.

公开(公告)号：US10380339B1

公开(公告)日：2019-08-13

申请号：US14727495

申请日：2015-06-01

Applicant: Amazon Technologies, Inc.

Inventor： Srikar Appalaraju ,  Amol Wanjari ,  Amit Arora ,  Vipul Bhargava ,  Ashish Hari Chiplunkar ,  Vineet Khare ,  Chellappan Lakshmanan

IPC: G06F12/14 ,  G06F21/56

Abstract: Techniques are disclosed herein for reactively identifying software products, available from an electronic marketplace, that are exhibiting anomalous behavior. Data associated with software products is accessed and analyzed to determine anomalous behavior. The data analyzed may include, but is not limited to, crash data, ratings data, marketplace data, usage data, and the like. A machine learning mechanism may be used to classify the application into a category relating to whether a potential anomaly is identified for the software product. A score may also be calculated for the software applications that indicates a severity of the anomalous behavior. The classification and/or the score may be used to determine whether to perform further analysis or testing with regard to a software product. For instance, the score may be used to determine that the software product is to be tested by a testing service.