Feedback-Driven Semi-Supervised Synthesis of Program Transformations

While editing code, it is common for developers to make multiple related repeated edits that are all instances
of a more general program transformation. Since this process can be tedious and error-prone, we study the
problem of automatically learning program transformations from past edits, which can then be used to predict
future edits. We take a novel view of the problem as a semi-supervised learning problem: apart from the
concrete edits that are instances of the general transformation, the learning procedure also exploits access
to additional inputs (program subtrees) that are marked as positive or negative depending on whether the
transformation applies on those inputs. We present a procedure to solve the semi-supervised transformation
learning problem using anti-unification and programming-by-example synthesis technology. To eliminate
reliance on access to marked additional inputs, we generalize the semi-supervised learning procedure to a
feedback-driven procedure that also generates the marked additional inputs in an iterative loop. We apply these
ideas to build and evaluate three applications that use different mechanisms for generating feedback. Compared
to existing tools that learn program transformations from edits, our feedback-driven semi-supervised approach
is vastly more effective in successfully predicting edits with significantly lesser amounts of past edit data.