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In this paper we present a novel, data-driven algorithm for generating feedback for students on open-ended programming problems. The feedback goes beyond next-step hints, annotating a student's whole program with suggested edits, including code that should be moved or reordered. We also build on existing work to design a methodology for evaluating this feedback in comparison to human tutor feedback, using a dataset of real student help requests. Our results suggest that our algorithm is capable of reproducing ideal human tutor edits almost as frequently as another human tutor. However, our algorithm also suggests many edits that are not supported by human tutors, indicating the need for better feedback selection. [For the full proceedings, see ED596512.]