Abstract
Automated program repair is a promising approach to deliver feedback to novice learners at scale. CLARA is an effective repairer that uses a correct program to fix an incorrect program. CLARA suffers from two main issues: rigid matching and lack of support for typical constructs and tasks in introductory programming assignments. We present several modifications to CLARA to overcome these problems. We propose approximate graph matching based on semantic and topological information of the programs compared, and modify CLARA’s abstract syntax tree processor and interpreter to support new constructs and tasks like reading from/writing to console. Our experiments show that, thanks to our modifications, we can apply CLARA to real-world programs. Also, our approximate graph matching allows us to repair many incorrect programs that are not repaired using rigid program matching.
This material is based upon work supported by the National Science Foundation under Grant No. 1915404.
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References
Approximate graph matching for CLARA. github.com/mcontractor/clara/
CLuster And RepAir tool for introductory programming assignments. github.com/iradicek/clara/
Less or equal. codeforces.com/problemset/problem/977/C
Who’s opposite? codeforces.com/problemset/problem/1560/B
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Contractor, M.R., Rivero, C.R. (2022). Improving Program Matching to Automatically Repair Introductory Programs. In: Crossley, S., Popescu, E. (eds) Intelligent Tutoring Systems. ITS 2022. Lecture Notes in Computer Science, vol 13284. Springer, Cham. https://doi.org/10.1007/978-3-031-09680-8_30
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DOI: https://doi.org/10.1007/978-3-031-09680-8_30
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