Intelligent automated test case generation method and apparatus
AssigneeVerizon Patent and Licensing Inc.
InventorsPallavi AGGRAWAL, Annu SINGH
Life of this patent
Estimating the term…
Abstract
Techniques for intelligent automated test case generation are disclosed. In one embodiment, a method is disclosed comprising generating a testing context comprising information identifying at least one difference between first and second code commit versions, generating model input using the testing context, generating a testing script using a trained natural language processing (NLP) model and the model input, and testing the second code commit version using the generated testing script.
Claims20
A method comprising:
- generating, by a computing device, a testing context comprising information identifying at least one difference between first and second code commit versions;
- generating, by the computing device, model input using the testing context;
- generating, by the computing device, a testing script using a trained natural language processing (NLP) model and the model input;
- and
- testing, by the computing device, the second code commit version using the generated testing script.
A non-transitory computer-readable storage medium tangibly encoded with computer-executable instructions that when executed by a processor associated with a computing device perform a method comprising:
- generating a testing context comprising information identifying at least one difference between first and second code commit versions;
- generating model input using the testing context;
- generating a testing script using a trained natural language processing (NLP) model and the model input;
- and
- testing the second code commit version using the generated testing script.
A computing device comprising:
- a processor, configured to:
- generate a testing context comprising information identifying at least one difference between first and second code commit versions;
- generate model input using the testing context;
- generating a testing script using a trained natural language processing (NLP) model and the model input;
- and
- test the second code commit version using the generated testing script.
Dependencies are inferred from the claim text.