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Active learning for efficient testing of student programs

Rastogi, I and Kanade, A and Shevade, S (2018) Active learning for efficient testing of student programs. In: 19th International Conference on Artificial Intelligence in Education, AIED 2018, 27 - 30 June 2018, London, pp. 296-300.

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Official URL: https://doi.org/10.1007/978-3-319-93846-2_55


In this work, we propose an automated method to identify semantic bugs in student programs, called ATAS, which builds upon the recent advances in both symbolic execution and active learning. Symbolic execution is a program analysis technique which can generate test cases through symbolic constraint solving. Our method makes use of a reference implementation of the task as its sole input. We compare our method with a symbolic execution-based baseline on 6 programming tasks retrieved from CodeForces comprising a total of 23 K student submissions. We show an average improvement of over 2.5x over the baseline in terms of runtime (thus making it more suitable for online evaluation), without a significant degradation in evaluation accuracy.

Item Type: Conference Paper
Publication: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publisher: Springer Verlag
Additional Information: The copyright for this article belongs to the Springer Verlag.
Keywords: Artificial intelligence; Model checking; Program debugging; Semantics; Software testing, Active Learning; Automated testing; Constraint Solving; Evaluation accuracy; On-line evaluation; Reference implementation; Student project; Symbolic execution, Students
Department/Centre: Division of Electrical Sciences > Computer Science & Automation
Date Deposited: 02 Sep 2022 10:24
Last Modified: 02 Sep 2022 10:24
URI: https://eprints.iisc.ac.in/id/eprint/76368

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