A computer-implemented method for automating analytical model building for code snippets, the method being executed by one or more processors and comprising: receiving, from an integrated development environment, IDE (220; 320), software instructions in natural language indicating functionality of a software component, wherein the software instructions are received based on the software component being checked into a source code repository associated with the IDE (220; 320), and wherein the functionality of the software component indicates a security vulnerability of the software component; fetching the software instructions from the source code repository; parsing and separating the software instructions into code blocks, based on annotations or code markers in the software instructions; performing pattern matching on the separated code blocks; classifying each of the separated code blocks into one or more categories based on a match with standard or example code snippets; identifying one or more security vulnerabilities in one or more of the separated code blocks; analyzing the one or more code blocks having the one or more security vulnerabilities through natural language processing based on machine learning, NLP, to identify tags to characterize the one or more code blocks having the one or more security vulnerabilities; performing a search, based on the identified tags, of relevant Internet sites to identify code snippets that are directed to fixing the one or more security vulnerabilities; scoring one or more of the identified code snippets based on a respective relevance to the one or more security vulnerabilities; based on the scoring of the one or more of the identified code snippets, determining that at least one of the code snippets has a score that meets a relevance threshold; providing, to the IDE (220; 320), the at least one of the code snippets based on the determination that the at least one of the code snippets has the score that meets the relevance threshold. receiving, from an integrated development environment, IDE (220; 320), software instructions in natural language indicating functionality of a software component, wherein the software instructions are received based on the software component being checked into a source code repository associated with the IDE (220; 320), and wherein the functionality of the software component indicates a security vulnerability of the software component; fetching the software instructions from the source code repository; parsing and separating the software instructions into code blocks, based on annotations or code markers in the software instructions; performing pattern matching on the separated code blocks; classifying each of the separated code blocks into one or more categories based on a match with standard or example code snippets; identifying one or more security vulnerabilities in one or more of the separated code blocks; analyzing the one or more code blocks having the one or more security vulnerabilities through natural language processing based on machine learning, NLP, to identify tags to characterize the one or more code blocks having the one or more security vulnerabilities; performing a search, based on the identified tags, of relevant Internet sites to identify code snippets that are directed to fixing the one or more security vulnerabilities; scoring one or more of the identified code snippets based on a respective relevance to the one or more security vulnerabilities; based on the scoring of the one or more of the identified code snippets, determining that at least one of the code snippets has a score that meets a relevance threshold; providing, to the IDE (220; 320), the at least one of the code snippets based on the determination that the at least one of the code snippets has the score that meets the relevance threshold.