Systems and methods for artificial intelligence tool for software code development that estimates runtime processing efficiency of serverless applications
AssigneeCapital One Services, LLC
InventorsSruthi RUDRAVAJJALA, Anne MOSHYEDI, Michael WANG
Life of this patent
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Abstract
Systems and methods for uses and/or improvements to artificial intelligence applications, particularly in the realm of code development. As one example, systems are described herein for an artificial intelligence tool for software code development that estimates runtime processing efficiency of serverless applications as well as provide recommendations for more efficient code for the serverless applications.
Claims20
A system for software code development that estimates runtime processing efficiency of serverless applications to provide recommendations for more efficient code for the serverless applications, the system comprising:
- one or more processors;
- and
- one or more non-transitory, computer-readable mediums, comprising instructions that, when executed by one or more processors, cause operations comprising:
- retrieving a first code base of a first user from a first computing service provided by a cloud platform;
- parsing the first code base to determine a first set of lambda functions in the first code base;
- performing one or more tracing operations on the first code base to determine respective runtime processing efficiencies for the first set of lambda functions;
- generating training data by labeling the first set of lambda functions with the respective runtime processing efficiencies;
- training, using the training data, a first artificial intelligence model to generate outputs of recommendations for alternative lambda functions in response to receiving inputs of code samples;
- receiving a first code sample for a first serverless application;
- identifying a first lambda function in the first code sample;
- generating a first feature input for the first artificial intelligence model based on the first code sample and the first lambda function;
- inputting the first feature input into the first artificial intelligence model to generate a first output;
- and
- generating for display, on a user interface, first recommendation comprising a second lambda function for replacing the first lambda function in the first code sample.
A method for software code development that estimates runtime processing efficiency of serverless applications to provide recommendations for more efficient code for the serverless applications, the method comprising:
- retrieving a first artificial intelligence model, wherein the first artificial intelligence model is trained to generate outputs of recommendations for alternative lambda functions in response to receiving inputs of code samples by:
- retrieving a first code base of a first user;
- parsing the first code base to determine a first set of lambda functions;
- determining respective runtime processing efficiencies for the first set of lambda functions;
- and
- generating training data by labeling the first set of lambda functions with the respective runtime processing efficiencies;
- receiving a first code sample;
- identifying a first lambda function in the first code sample;
- generating a first feature input for the first artificial intelligence model based on the first code sample and the first lambda function;
- inputting the first feature input into the first artificial intelligence model to generate a first output;
- and
- based on the first output, generating for display, on a user interface, first recommendation comprising a second lambda function for replacing the first lambda function in the first code sample.
One or more non-transitory, computer-readable mediums, comprising instructions that, when executed by one or more processors, cause operations comprising:
- retrieving a first artificial intelligence model, wherein the first artificial intelligence model is trained to generate outputs of recommendations for alternative lambda functions in response to receiving inputs of code samples by:
- retrieving a first code base of a first user;
- parsing the first code base to determine a first set of lambda functions;
- determining respective runtime processing efficiencies for the first set of lambda functions;
- and
- generating training data by labeling the first set of lambda functions with the respective runtime processing efficiencies;
- receiving a first code sample;
- identifying a first lambda function in the first code sample;
- inputting the first code sample and the first lambda function into the first artificial intelligence model to generate a first output;
- and
- based on the first output, generating for display, on a user interface, first recommendation comprising a second lambda function for replacing the first lambda function in the first code sample.
Dependencies are inferred from the claim text.