Deep Reinforcement Learning for Programming Language Correction

TL;DR

This paper introduces a reinforcement learning-based framework for correcting programming language errors, enabling an agent to learn text editing actions directly from raw code without formal syntax knowledge, improving correction rates over existing methods.

Contribution

The paper presents a novel reinforcement learning approach for programming language correction that learns from raw code and uses expert demonstrations to enhance training efficiency.

Findings

Fixes 14% more erroneous programs than DeepFix.

Resolves 29% more compiler error messages.

Operates without requiring formal syntax knowledge.

Abstract

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.