US 10,949,337 B1Granted

Utilizing neural network and artificial intelligence models to select and execute test cases in a software development platform

AssigneeAccenture Global Solutions Limited

InventorsMuralidhar Yalla, Anand Vijendra, Venugopal Ramakrishnan

Life of this patent

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Abstract

A device may receive historical software data associated with a software development platform, and may train a neural network model, with the historical software data, to generate a trained neural network model. The device may receive, from the software development platform, historical test configuration data, and may train an execution model, with the historical test configuration data, to generate a trained execution model. The device may receive software data identifying software to be tested, and may process the software data, with the trained neural network model, to predict a set of test cases to execute for testing the software. The device may process the set of test cases, with the trained execution model, to identify configurations, scripts, and test targets for executing the set of test cases, and may perform one or more actions based on the set of test cases, the configurations, the scripts, and the test targets.

Claims20

Dependent claims

A method, comprising:

receiving, by a device, historical software data associated with a software development platform;
training, by the device, a neural network model, with the historical software data, to generate a trained neural network model;
receiving, by the device and from the software development platform, historical test configuration data,
wherein the historical test configuration data includes data identifying:
historical sets of test cases,
historical types of executions performed for the historical sets of test cases, and
historical target devices utilized to perform the executions;
training, by the device, an execution model, with the historical test configuration data, to generate a trained execution model;
receiving, by the device, software data identifying software to be tested in the software development platform;
processing, by the device, the software data, with the trained neural network model, to predict a set of test cases to execute for testing the software in the software development platform;
processing, by the device, the set of test cases, with the trained execution model, to identify one or more of configurations, scripts, or test targets for executing the set of test cases in the software development platform;
and
performing, by the device, one or more actions based on the set of test cases and the one or more of the configurations, the scripts, or the test targets.

10 more elements

A device, comprising:

one or more memories;
and
one or more processors, communicatively coupled to the one or more memories, configured to:
receive software data identifying software to be tested in a software development platform;
process the software data, with a neural network model, to predict a set of test cases to execute for testing the software in the software development platform,
wherein the neural network model is trained based on historical software data associated with the software development platform;
process the set of test cases, with an execution model, to identify configurations, scripts, and test targets for executing the set of test cases in the software development platform,
wherein the execution model is trained based on historical test configuration data,
wherein the historical test configuration data includes data identifying:
historical sets of test cases,
historical types of executions performed for the historical sets of test cases, and
historical target devices utilized to perform the executions;
and
perform one or more actions based on the set of test cases, the configurations, the scripts, and the test targets.

11 more elements

A non-transitory computer-readable medium storing instructions, the instructions comprising:

one or more instructions that, when executed by one or more processors, cause the one or more processors to:
receive historical software data associated with a software development platform;
train a neural network model, with the historical software data, to generate a trained neural network model;
receive, from the software development platform, historical test configuration data,
wherein the historical test configuration data includes data identifying:
historical sets of test cases,
historical types of executions performed for the historical sets of test cases, and
historical target devices utilized to perform the executions;
train an execution model, with the historical test configuration data, to generate a trained execution model;
receive software data identifying software to be tested in the software development platform;
process the software data, with the trained neural network model, to predict a set of test cases to execute for testing the software in the software development platform;
process the set of test cases, with the trained execution model, to identify configurations, scripts, and test targets for executing the set of test cases in the software development platform;
and
cause the set of test cases to be executed in the software development platform based on the configurations, the scripts, and the test targets.

11 more elements

Dependencies are inferred from the claim text.

Facts

Application: 16/667,255Filed 29 Oct 2019
Granted: 16 Mar 2021
Term ends: Not estimatedThe source records don’t support an estimate.

Classification11 codes

G06F 11/3688
Test management: for test execution, e.g. scheduling of test suites
G06F 11/3698
Prevention of errors by analysis, debugging or testing of software: environments for analysis, debugging or testing of software
G06N 3/0442
Recurrent networks, e.g. Hopfield networks: characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU]
G06N 3/0499
Architecture, e.g. interconnection topology: feedforward networks