Deep Reinforcement Learning for Syntactic Error Repair in Student Programs
Open accessAbstract
Novice programmers often struggle with the formal syntax of programming languages. In the traditional classroom setting, they can make progress with the help of real time feedback from their instructors which is often impossible to get in the massive open online course (MOOC) setting. Syntactic error repair techniques have huge potential to assist them at scale. Towards this, we design a novel programming language correction framework amenable to reinforcement learning. The framework allows an agent to mimic human actions for text navigation and editing. We demonstrate that the agent can be trained through self-exploration directly from the raw input, that is, program text itself, without either supervision or any prior knowledge of the formal syntax of the programming language. We evaluate our technique on a publicly available dataset containing 6975 erroneous C programs with typographic errors, written by students during an introductory programming course. Our technique fixes 1699 (24.4%) programs completely and 1310 (18.8%) program partially, outperforming DeepFix, a state-of-the-art syntactic error repair technique, which uses a fully supervised neural machine translation approach.
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Artificial intelligence and machine learning approaches have been used for finding and repairing errors in programs, but only very fundamental approaches have been applied to researching programming error messages.
“Artificial intelligence and machine learning approaches have been used for finding and repairing errors in programs [1,20,21] but only very fundamental approaches have been applied to researching PEMs [11].”
From Using Large Language Models to Enhance Programming Error Messages (2023) · cites this paper as [20]
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- Authors
- Gupta, R., Kanade, A., & Shevade, S.
- Affiliations
- Indian Institute of Science Bangalore (IN)
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2019-07-17
- Volume
- 33
- Issue
- 01
- Pages
- 930-937
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W2905087861
- Cited by
- 54
- References
- 31
- Citations by year
- 2026: 2 · 2025: 6 · 2024: 5 · 2023: 16 · 2022: 10 · 2021: 8 · 2020: 6 · 2019: 1
- FWCI
- 7.23
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- www.aaai.org/ (unspecified)
- Locations
- Proceedings of the AAAI Conference on Artificial Intelligence (publishedVersion)
- NOT FOUND REPOSITORY (Indian Institute of Science Bangalore) (acceptedVersion)
- AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
- Physical Sciences › Computer Science › Information Systems › Software Engineering Research
- Physical Sciences › Computer Science › Software › Software Testing and Debugging Techniques
- Keywords
- deep reinforcement learning, novice programmers, student programming, C programs, typographical errors, self-exploration, MOOCs
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