Sauce Labs Inc.

Fernando Vidal

Bradley Scott Adelberg

Benjamin H Ellis

Christian Bromann

Jean Sandberg

Robert Henrikson

Machine learning techniques are employed to model test runs of an automated test platform in ways that allow for reliable identification of various types of test behavior such as, for example, whether certain classes of failures can be characterized as test flake.

Analytics for an automated application testing platform

Methods and apparatus are described by which artificial intelligence (AI) is used to enable the rapid development of reliable test suites for web and mobile applications. An AI agent guided by reinforcement learning explores an application-under-test (AUT), interacting with the AUT to traverse the flows through the AUT by seeking novel application states. A subset of these flows is then identified as being representative of the functionality of the AUT. The interactions between the AI agent and the AUT that define these identified flows form the basis for the test suite.

Authoring automated test suites using artificial intelligence

Methods and apparatus are described by which time-series data captured during the automated testing of software applications may be analyzed. Change-point detection is used to partition the time-series data, and an expected variance of data within each partition is determined. Because the partitioning of the test data provides a high level of confidence that the data points in a given partition conform to the same distribution, data points that represent meaningful changes in application performance can be more confidently and efficiently identified.

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Oakland, CA, United States of America

Fernando Vidal