Mining on Students’ Execution Logs and Repairing Compilation Errors Based on Deep Learning

Abstract

Automatic program repair techniques based on deep neural networks have attracted widespread attention from researchers due to the high degree of automation and generality. However, there is a scarcity of high-quality labeled datasets available for training program repair models. This study proposes a method of mining reasonable program repair examples from student program execution logs. Additionally, we introduce the Rookie Simulator (RS), which simulates the error patterns commonly made by novice programmers and generates a large number of program repair sample pairs. To address the issue of low repair rates for infrequent and complex error patterns in compilation errors, the study proposes the attention-enhanced capsule network for program repair (ACNPR), a program repair model that integrates compiler feedback information and utilizes capsule networks to capture complex semantic features. Experimental evaluations were conducted using publicly available datasets, including the DeepFix, TEGCER, and a real course dataset named SUES-COJ mined in this study. The results indicate that our method consistently outperforms current state-of-the-art models in terms of full repair rates.