Papers › Deep Reinforcement Learning for Programming Language Correction

Deep Reinforcement Learning for Programming Language Correction

31 Jan 2018arXiv:1801.10467archive 2025-07-28

Rahul Gupta, Aditya Kanade, Shirish Shevade

Novice programmers often struggle with the formal syntax of programming languages. To assist them, 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 any knowledge of the formal syntax of the programming language. We leverage expert demonstrations for one tenth of the training data to accelerate training. The proposed technique is evaluated on 6975 erroneous C programs with typographic errors, written by students during an introductory programming course. Our technique fixes 14% more programs and 29% more compiler error messages relative to those fixed by a state-of-the-art tool, DeepFix, which uses a fully supervised neural machine translation approach.

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terne/dtuproject mentioned on GitHubpytorch report

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Tasks

Deep Reinforcement Learning Machine Translation Program Repair Reinforcement Learning Reinforcement Learning (RL)Translation reinforcement-learning

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Program Repair	DeepFix	RLAssist	Average Success Rate	26.6	#4 of 4	Archive leaderboard	report

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