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