Sas Institute Inc.

Yu-Min Lin

Joshua David Griffin

Yan Gao

Jorge Manuel Gomes Da Silva

Yan Xu

Seyedalireza Yektamaram

Raymond Eugene Wright

Ricky Dee Tharrington, Jr

Kiran Devidas Bhole

Girish Vinayak Kolapkar

Aishwarya Sharma

Amod Anil Ankulkar

Pei-Yi Tan

Bengt Wisen Pederson

Kushawah Yogender Singh

A computing device selects new test configurations for testing software. Software under test is executed with first test configurations to generate a test result for each test configuration. Each test configuration includes a value for each test parameter where each test parameter is an input to the software under test. A predictive model is trained using each test configuration of the first test configurations in association with the test result generated for each test configuration based on an objective function value. The predictive model is executed with second test configurations to predict the test result for each test configuration of the second test configurations. Test configurations are selected from the second test configurations based on the predicted test results to define third test configurations. The software under test is executed with the defined third test configurations to generate the test result for each test configuration of the third test configurations.

Automated machine learning test system

A computing device determines an optimal number of threads for a computer task. Execution of a computing task is controlled in a computing environment based on each task configuration included in a plurality of task configurations to determine an execution runtime value for each task configuration. An optimal number of threads value is determined for each set of task configurations having common values for a task parameter value, a dataset indicator, and a hardware indicator. The optimal number of threads value is an extremum value of an execution parameter value as a function of a number of threads value. A dataset parameter value is determined for a dataset. A hardware parameter value is determined as a characteristic of each distinct executing computing device in the computing environment. The optimal number of threads value for each set of task configurations is stored in a performance dataset in association with the common values.

Optimal number of threads determination system

Growing community of inventors

Raleigh, NC, United States of America

Yu-Min Lin