Abstract: Historic context data is automatically associated with particular pieces of source code by retrieval data structures. Ephemeral information is preserved, such as how a piece of code originated operationally and was changed over time, which research sources informed the code's origination and changes, and why particular changes in the code were made. Code may be rolled back to an earlier version based on parameters such as whether code had been refactored, or results of testing or static analysis. Rollback goes beyond editor undo actions, and a developer need not specify a timestamp or a version number. Developer documentation burdens are reduced, developer understanding is increased, and code quality is enhanced, by providing ready access to the code's software development context history data. Some actions made possible include highlighting code that was generated automatically by autocompletion or otherwise, highlighting refactored code, and highlighting pasted code, among other actions.

Abstract: A source code generation system uses a neural transformer model with attention to predict candidate method bodies given a method docstring, method signature, and one or more method templates. The method templates are derived from intent-snippet pairs from StackOverflow question/answer pairs or template methods from GitHub. Joint embeddings are generated for the method bodies of the method templates and associated method docstrings for quick retrieval. A code completion system uses the source code generation system to generate candidate method bodies to complete a method signature and/or method docstring using the method templates.

Abstract: An example of apparatus includes a memory to store a first image of a document and a second image of the document. The first image and the second image are Memory captured under different conditions. The apparatus includes a processor coupled to the memory. The processor is to perform optical character recognition on the first image to generate a first output dataset and to perform optical character recognition on the second image to generate a second output dataset. The processor is further to determine whether consensus for a character is achieved based on a comparison of the first output dataset with the second output dataset, and generate a final output dataset based on the consensus for the character.

Abstract: An example apparatus including memory to store a first image of a document and a second image of the document, and a processor coupled to the memory, wherein the processor is to: perform optical character recognition on the first image to generate a first output dataset; perform optical character recognition on the second image to generate a second output dataset; and compute a transformation matrix based on the first output dataset and the second output dataset, the transformation matrix to align the first image with the second image.

Abstract: An example apparatus including memory to store a first image of a document and a second image of the document, and a processor coupled to the memory, wherein the processor is to: perform optical character recognition on the first image to generate a first output dataset; perform optical character recognition on the second image to generate a second output dataset; and compute a transformation matrix based on the first output dataset and the second output dataset, the transformation matrix to align the first image with the second image.

Abstract: An example of apparatus includes a memory to store a first image of a document and a second image of the document. The first image and the second image are Memory captured under different conditions. The apparatus includes a processor coupled to the memory. The processor is to perform optical character recognition on the first image to generate a first output dataset and to perform optical character recognition on the second image to generate a second output dataset. The processor is further to determine whether consensus for a character is achieved based on a comparison of the first output dataset with the second output dataset, and generate a final output dataset based on the consensus for the character.