RePurr: Automated Repair of Block-Based Learners’ Programs

Programming is increasingly taught using dedicated block-based programming environments such as Scratch. While the use of blocks instead of text prevents syntax errors, learners can still make semantic mistakes implying a need for feedback and help. Since teachers may be overwhelmed by help requests in a classroom, may not have the required programming education themselves, and may simply not be available in independent learning scenarios, automated hint generation is desirable. Automated program repair can provide the foundation for automated hints, but relies on multiple assumptions: (1) Program repair usually aims to produce localized patches for fixing single bugs, but learners may fundamentally misunderstand programming concepts and tasks or request help for substantially incomplete programs. (2) Software tests are required to guide the search and to localize broken statements, but test suites for block-based programs are different to those considered in past research on fault localization and repair: They consist of system tests, where very few tests are sufficient to fully cover the code. At the same time, these tests have vastly longer execution times caused by the use of animations and interactions on Scratch programs, thus inhibiting the applicability of metaheuristic search. (3) The plastic surgery hypothesis assumes that the code necessary for repairs already exists in the codebase. Block-based programs tend to be small and may lack this necessary redundancy. In order to study whether automated program repair of block-based programs is nevertheless feasible, in this paper we introduce, to the best of our knowledge, the first automated program repair approach for Scratch programs based on evolutionary search. Our RePurr prototype includes novel refinements of fault localization to improve the lack of guidance of the test suites, recovers the plastic surgery hypothesis by exploiting that a learning scenario provides model and student solutions as alternatives, and uses parallelization and accelerated executions to reduce the costs of fitness evaluations. Empirical evaluation of RePurr on a set of real learners' programs confirms the anticipated challenges, but also demonstrates that the repair can nonetheless effectively improve and fix learners' programs, thus enabling automated generation of hints and feedback for learners.