FEEDBACK-DRIVEN SEMI-SUPERVISED SYNTHESIS OF PROGRAM TRANSFORMATIONS

摘要

A synthesis procedure learns program transformations for a text document, on-the-fly during an edit session, from examples of concrete edits made during the edit session and from an unsupervised set of additional inputs. The additional inputs are derived from explicit feedback from the user and inferred feedback from the user's behavior during the edit session. A reward score, based on anti-unification and provenance analysis, is used to classify the additional inputs as either a positive input or a negative input. Outputs are generated for the positive inputs that are consistent with the existing examples and then used to synthesize a new program transformation. The program transformations are then used to generate code edit suggestions during the edit session.