Abstract: Techniques for generating rules from documentation are described. For example, a method for generating rules may include generating one or more rules from documentation by: extracting a plurality of chunks from the documentation, inferring one or more candidate rules from the extracted chunks, mining the inferred one or more candidate rules to determine at least one of the one or more candidate rules is to be included in rule generation, classifying the at least one mined one or more candidate rules as one or more rules, and extracting information to generate the one or more rules.

Abstract: Techniques for unsupervised learning of embeddings on source code from non-local contexts are described. Code can be processed to generate an abstract syntax tree (AST) which represents syntactic paths between tokens in the code. Once the AST(s) have been generated, the paths in the AST(s) can be crawled to identify terminals (e.g., leaf nodes in the AST) and paths between terminals can be identified. The pairs of tokens identified at the ends of each path can then be used to generate a cooccurrence matrix. For example, if X number of unique terminals are identified, a matrix of size X by X can be generated to indicate a frequency at which pairs of terminals cooccur. This cooccurrence matrix can then be used as input to existing techniques for learning vector-space embeddings, such as word2vec, GloVe, Swivel, etc.