Flexible control flow graph alignment for delivering data-driven feedback to novice programming learners
Supporting learners in introductory programming assignments at scale is a necessity. This support includes automated feedback on what learners did incorrectly. Existing approaches cast the problem as automatically repairing learners’ incorrect programs extrapolating the data from an existing correct...
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| Veröffentlicht in: | The Journal of systems and software Jg. 210; S. 111960 |
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| Hauptverfasser: | , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Elsevier Inc
01.04.2024
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| Schlagworte: | |
| ISSN: | 0164-1212 |
| Online-Zugang: | Volltext |
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| Zusammenfassung: | Supporting learners in introductory programming assignments at scale is a necessity. This support includes automated feedback on what learners did incorrectly. Existing approaches cast the problem as automatically repairing learners’ incorrect programs extrapolating the data from an existing correct program from other learners. However, such approaches are limited because they only compare programs with similar control flow and order of statements. A potentially valuable set of repair feedback from flexible comparisons is thus missing. In this paper, we present several modifications to CLARA, a data-driven automated repair approach that is open source, to deal with real-world introductory programs. We extend CLARA’s abstract syntax tree processor to handle common introductory programming constructs. Additionally, we propose a flexible alignment algorithm over control flow graphs where we enrich nodes with semantic annotations extracted from programs using operations and calls. Using this alignment, we modify an incorrect program’s control flow graph to match correct programs to apply CLARA’s original repair process. We evaluate our approach against a baseline on the twenty most popular programming problems in Codeforces. Our results indicate that flexible alignment has a significantly higher percentage of successful repairs at 46% compared to 5% for baseline CLARA. Our implementation is available at https://github.com/towhidabsar/clara.
•We adapt a “search, align and repair” approach to fix novice learner programs.•We present a flexible program alignment scheme that enhances repair capabilities.•Our experiments use programs from twenty real-world programming assignments.•Our flexible scheme outperforms the original: 46% vs. 5% repair success rate.•Our implementation is available at https://github.com/towhidabsar/clara. |
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| ISSN: | 0164-1212 |
| DOI: | 10.1016/j.jss.2024.111960 |