Deep Reinforcement Learning for Programming Language Correction
This addresses the challenge of assisting novice programmers with syntax errors, though it is incremental as it builds on existing correction methods with a new training approach.
The paper tackles the problem of automatically correcting syntax errors in novice programmers' code by introducing a deep reinforcement learning framework that mimics human editing actions. The method fixes 14% more programs and 29% more compiler error messages than the state-of-the-art tool DeepFix on a dataset of 6975 erroneous C programs.
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.