Error Understanding in Program Code With LLM-DL for Multi-label Classification

TL;DR

This paper explores the use of fine-tuned Large Language Models combined with deep learning architectures to classify multiple error types in programming code, aiming to improve automated error detection and feedback.

Contribution

It introduces a multi-label error classification framework leveraging various LLMs and DL models, demonstrating superior performance in classifying programming errors.

Findings

CodeT5+_GRU achieved the highest weighted F1-score of 0.8243

Model ensemble improved multi-label classification accuracy

Results support combining pretrained encoders with recurrent decoders for error understanding.

Abstract

Programming is a core skill in computer science and software engineering (SE), yet identifying and resolving code errors remains challenging for both novice and experienced developers. While Large Language Models (LLMs) have shown remarkable capabilities in natural language understanding and generation tasks, their potential in domain-specific, complex scenarios, such as multi-label classification (MLC) of programming errors, remains underexplored. Recognizing this less-explored area, this study proposes a multi-label error classification (MLEC) framework for source code that leverages fine-tuned LLMs, including CodeT5-base, GraphCodeBERT, CodeT5+, UniXcoder, RoBERTa, PLBART, and CoTexT. These LLMs are integrated with deep learning (DL) architectures such as GRU, LSTM, BiLSTM, and BiLSTM with an additive attention mechanism (BiLSTM-A) to capture both syntactic and semantic features from a real-world student-written Python code error dataset. Extensive experiments across 32 model variants, optimized using Optuna-based hyperparameter tuning, have been evaluated using comprehensive multi-label metrics, including average accuracy, macro and weighted precision, recall, F1-score, exact match accuracy, One-error, Hamming loss, Jaccard similarity, and ROC-AUC (micro, macro, and weighted). Results show that the CodeT5+\_GRU model achieved the strongest performance, with a weighted F1-score of 0.8243, average accuracy of 91.84\%, exact match accuracy of 53.78\%, Hamming loss of 0.0816, and One error of 0.0708. These findings confirm the effectiveness of combining pretrained semantic encoders with efficient recurrent decoders. This work lays the foundation for developing intelligent, scalable tools for automated code feedback, with potential applications in programming education (PE) and broader SE domains.