Development test automation framework

A method comprising:

• at a computing platform comprising at least one processor, a communication interface, and memory:
• training, based on historical error data, a machine learning engine, wherein training the machine learning engine configures the machine learning engine to identify, based on the historical error data, error remediation information comprising an action to correct an identified error and a confidence score corresponding to the action;
• automatically identifying a first error in an application by executing a plurality of test cases, specific to the application, wherein the plurality of test cases are stored at the computing platform using a plain language format;
• inputting the first error into the machine learning engine to output, for the first error, first error remediation information corresponding to the first error, wherein the first error remediation information comprises a first action to correct the first error;
• automatically correcting the first error using the first action;
• and
• sending, once the first error has been corrected, one or more commands directing a data storage system to store an indication that the first error has been corrected, wherein sending the one or more commands directing a data storage system to store the indication that the first error has been corrected causes the data storage system to store the indication that the first error has been corrected.

One or more non-transitory computer-readable media storing instructions that, when executed by a computing platform comprising at least one processor, a communication interface, and memory, cause the computing platform to:

• train, based on historical error data, a machine learning engine, wherein training the machine learning engine configures the machine learning engine to identify, based on the historical error data, error remediation information comprising an action to correct an identified error and a confidence score corresponding to the action;
• automatically identify a first error in an application by executing a plurality of test cases, specific to the application, wherein the plurality of test cases are stored at the computing platform using a plain language format;
• input the first error into the machine learning engine to output, for the first error, first error remediation information corresponding to the first error, wherein the first error remediation information comprises a first action to correct the first error;
• automatically correct the first error using the first action;
• and
• send, once the first error has been corrected, one or more commands directing a data storage system to store an indication that the first error has been corrected, wherein sending the one or more commands directing a data storage system to store the indication that the first error has been corrected causes the data storage system to store the indication that the first error has been corrected.