Identifying the translatability of hard-coded strings in source code via POS tagging

A computer program product for localizing software, the computer program product comprising a computer-readable storage medium having computer-usable program code embodied therein, the computer-usable program code configured to perform the following when executed by at least one processor:

• prepare a set of training data, wherein the preparing the set of training data comprises:
• parsing source code and associated localization resource files to identify hard-coded strings and their associated contexts;
• based on the associated contexts, provide a confidence score for each hard-coded string indicating a likelihood of the hard-coded string being translatable, wherein the confidence score is determined at least in part based on the associated context;
• based on the confidence score for each hard-coded string, transforming each hard-coded string into a single equivalence word from a set of equivalence words mapped to confidence scores;
• tagging the hard-coded strings in the source code and associated localization resource files as one of translatable and non-translatable, wherein the tagging tags the single equivalence word of each hard-coded string;
• and
• build positive training data using resourced-out strings from the hard-coded strings tagged as translatable;
• train a parts-of-speech (POS) tagging model using the training data;
• at runtime, by the POS tagging model, locate translatable hard-coded strings in source code of the software, wherein the locating comprises fetching potential hard-coded strings and their associated contexts and tagging each potential hard-coded string as one of translatable and non-translatable using the POS tagging model:
• translate the located translatable hard-coded strings;
• and
• adapt elements of a user interface using the translated hard-coded strings.

A system for localizing software, the system comprising:

• at least one processor;
• and
• at least one memory device operably coupled to the at least one processor and storing instructions for execution on the at least one processor, the instructions causing the at least one processor to:
• prepare a set of training data, wherein the instructions causing the at least one processor to prepare the set of training data comprise instructions causing the at least one processor to:
• parse source code and associated localization resource files to identify hard-coded strings and their associated context;
• provide a confidence score for each hard-coded string indicating a likelihood of the hard-coded string being translatable, wherein the confidence score is determined at least in part based on the associated context;
• based on the confidence score for each hard-coded string, transform each hard-coded string into a single equivalence word from a set of equivalence words mapped to confidence scores;
• tag the hard-coded strings in the source code and associated localization resource files as one of translatable and non-translatable, wherein the tagging tags the single equivalence word of each hard-coded string;
• and
• build positive training data using resourced-out strings from the hard-coded strings tagged as translatable;
• train a parts-of-speech (POS) tagging model using the training data;
• at runtime, by the POS tagging model, locate translatable hard-coded strings in source code of the software, wherein the locating comprises fetching potential hard-coded strings and their associated contexts and tagging each potential hard-coded string as one of translatable and non-translatable using the POS tagging model;
• translate the located translatable hard-coded strings;
• and
• adapt elements of a user interface using the translated hard-coded strings.