Systems and methods for machine learning-based execution of software code from scientific or technical documents in a virtual environment

A system for executing software code associated with scientific or technical documents in a virtual environment, the system comprising:

• at least one memory;
• and
• at least one hardware processor coupled with the at least one memory and configured, individually or in combination, to:
• receive a scientific or technical document and software code associated with the document, wherein the software code comprises executable code and/or source code;
• analyze the document and/or software code using a first prepared machine learning (ML) model to identify hardware, software and/or runtime configuration parameters for execution of the software code;
• configure the virtual environment based at least on one or more identified hardware, software and/or runtime configuration parameters;
• execute the software code in the virtual environment and collecting an execution log of the software code;
• and
• in the event the software code fails to execute correctly,
• analyze the execution log using a second prepared MLM to identify configuration parameters of the virtual environment associated with a cause of failure in execution of the software code,
• modify the identified configuration parameters of the virtual environment, and
• execute the software code in the modified virtual environment.

A non-transitory computer readable medium storing thereon computer executable instructions for executing software code associated with scientific or technical documents in a virtual environment, including instructions for:

• receiving a scientific or technical document and software code associated with the document, wherein the software code comprises executable code and/or source code;
• analyzing the document and/or software code using a first prepared machine learning (ML) model to identify hardware, software and/or runtime configuration parameters for execution of the software code;
• configuring the virtual environment based at least on one or more identified hardware, software and/or runtime configuration parameters;
• executing the software code in the virtual environment and collecting an execution log of the software code;
• and
• in the event the software code fails to execute correctly,
• analyzing the execution log using a second prepared MLM to identify configuration parameters of the virtual environment associated with a cause of failure in execution of the software code,
• modifying the identified configuration parameters of the virtual environment, and
• executing the software code in the modified virtual environment.