System and method for a machine learning source code generation via a holochain network

A computer program product for a decentralized machine learning source code generation framework via a holochain network, the computer program product comprising a non-transitory computer-readable medium comprising code causing an apparatus to:

• receive a predetermined code criteria of at least one selected from a group consisting of performance, security, complexity and scalability;
• receive a new code segment from an integrated development environment executing on an endpoint device;
• retrieve characteristics of the new code segment;
• determine, using a trained machine learning model, at least one subsequent code segment, based on at least the characteristics of the new code segment;
• determine, using an evaluation engine, at least one score of the subsequent code segment, wherein the at least one score is associated with the predetermined code criteria;
• and
• automatically transmit, over a network interface, a predetermined number of the at least one subsequent code segments above a predetermined threshold of the at least one score to the integrated development environment executing on the endpoint device for display,
• wherein the performance is determined based on a minimum number of CPU cycles needed to execute the subsequent code segment,
• wherein the security is determined based on a data flow analysis of the subsequent code segment,
• wherein the complexity is determined based on a complexity measure of the subsequent code segment,
• wherein the scalability is determined based on scalability performance testing the subsequent code segment.

A method for a decentralized machine learning source code generation framework via a holochain network, the method comprising:

• receiving a predetermined code criteria of at least one selected from a group consisting of performance, security, complexity and scalability;
• receiving a new code segment from an integrated development environment executing on an endpoint device;
• retrieving characteristics of the new code segment;
• determining, using a trained machine learning model, at least one subsequent code segment, based on at least the characteristics of the new code segment;
• determining, using an evaluation engine, at least one score of the subsequent code segment, wherein the at least one score is associated with the predetermined code criteria;
• and
• automatically transmitting, over a network interface, a predetermined number of the at least one subsequent code segments above a predetermined threshold of the at least one score to the integrated development environment executing on the endpoint device for display,
• wherein the performance is determined based on a minimum number of CPU cycles needed to execute the subsequent code segment,
• wherein the security is determined based on a data flow analysis of the subsequent code segment,
• wherein the complexity is determined based on a complexity measure of the subsequent code segment,
• wherein the scalability is determined based on scalability performance testing the subsequent code segment.