Automatic extraction of AST patterns for debugging student programs
When implementing a programming tutor, it is often difficult to manually consider all possible errors encountered by students. An alternative is to automatically learn a bug library of erroneous patterns from students’ programs.
We propose using abstract-syntax-tree (AST) patterns as features for learning rules to distinguish between correct and incorrect programs. These rules can be used for debugging student programs: rules for incorrect programs (buggy rules) contain patterns indicating mistakes, whereas rules for correct programs cover subsets of submissions sharing the same solution strategy.
To generate hints, we first check all buggy rules and point out incorrect patterns. If no buggy rule matches, rules for correct programs are used to recognize the student’s intent and suggest patterns that still need to be implemented.
We evaluated our approach on past student programming data for a number of Prolog problems. For 31 out of 44 problems, the induced rules correctly classified over 85% of programs based only on their structural features. For approximately 73% of incorrect submissions, we were able to generate hints that were implemented by the student in some subsequent submission.
Paper presented at the 18th conference on AI in Education.
