Intelligently identifying source code from compiled code

A computer-program product comprising one or more non-transitory machine-readable storage media, including stored instructions configured to cause a computing system to perform a set of actions including:

• disassembling a particular set of executable code to determine a particular set of translated code;
• for the particular set of translated code, generating a particular translated code vector embedding in an embedding space;
• for a plurality of sets of source code, generating a plurality of source code vector embeddings in the embedding space;
• accessing a machine learning model trained on training data comprising pairs of sets of translated code and known sets of source code to predict one or more particular sets of source code that are similar to the particular set of translated code based at least in part on vector distances between the plurality of source code vector embeddings and the particular translated code vector embedding in the embedding space;
• wherein the embedding space assigns different weights to different features based at least in part on which features were similar among the pairs in the training data;
• and
• causing display of information indicating the one or more particular sets of source code are associated with the particular set of executable code.

A system comprising:

• one or more processors;
• one or more non-transitory computer-readable media storing instructions, which, when executed by the system, cause the system to perform a set of actions including:
• disassembling a particular set of executable code to determine a particular set of translated code;
• for the particular set of translated code, generating a particular translated code vector embedding in an embedding space;
• for a plurality of sets of source code, generating a plurality of source code vector embeddings in the embedding space;
• accessing a machine learning model trained on training data comprising pairs of sets of translated code and known sets of source code to predict one or more particular sets of source code that are similar to the particular set of translated code based at least in part on vector distances between the plurality of source code vector embeddings and the particular translated code vector embedding in the embedding space;
• wherein the embedding space assigns different weights to different features based at least in part on which features were similar among the pairs in the training data;
• and
• causing display of information indicating the one or more particular sets of source code are associated with the particular set of executable code.

A computer-implemented method comprising:

• accessing a particular set of untranslated code;
• for the particular set of untranslated code, generating a particular untranslated code vector embedding in an embedding space;
• for a plurality of sets of translated code, generating a plurality of translated code vector embeddings in the embedding space;
• accessing a machine learning model trained on training data comprising pairs of sets of untranslated code and known sets of translated code to predict one or more particular sets of translated code that are similar to the particular set of untranslated code based at least in part on vector distances between the plurality of translated code vector embeddings and the particular untranslated code vector embedding in the embedding space;
• wherein the embedding space assigns different weights to different features based at least in part on which features were similar among the pairs in the training data;
• and
• causing display of information indicating the one or more particular sets of translated code are associated with the particular set of untranslated code.