.png){kind=icon}
返回
Multi-Label Code Error Classification Using CodeT5 and ML-KNN
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2024.3430558.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Programming is an essential skill in computer science and in a wide range of engineering-related disciplines. However, occurring errors, often referred to as bugs in code, can indeed be challenging to identify and rectify, both for students who are learning to program and for experienced professionals. These errors can lead to unexpected behaviors in programming. Understanding, finding, and effectively dealing with errors is an integral part of programming learning as well as software development. To classify the errors, we propose a multi-label error classification of source code for dealing with programming data by using the ML-KNN classifier with CodeT5 embeddings. In addition, several deep neural network (DNN) models, including GRU, LSTM, BiLSTM, and BiLSTM-A (attention mechanism) are also employed as baseline models to classify the errors. We trained all the models by using a large-scale dataset (original error labels) as well as modified datasets (summarized error labels) of the source code. The average classification accuracy of the proposed model is 95.91% and 84.77% for the original and summarized error-labeled datasets, respectively. The exact match accuracy is 22.57% and 27.22% respectively for the original and summarized error-labeled datasets. The comprehensive experimental results of the proposed approach are promising for multi-label error classification over the baseline models. Moreover, the findings derived from the proposed approach and data-driven analytical results hold significant promise for error classification, programming education, and related research endeavors.
Keyword:
Programming profession
Codes
Accuracy
Source coding
Education
Task analysis
Machine learning
Big Data
Error analysis
Learning systems
Software engineering
CodeT5
data analysis
educational big data
error classification
learning analytics
ML-KNN
multi-label classification
programming learning
software engineering
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
Identifying algorithm in program code based on structural features using CNN classification model基于CNN分类模型的程序代码结构特征识别算法.png)
APPLIED INTELLIGENCEOAAI
APPLIED INTELLIGENCEIF3.5
OAAI分享收藏A recommendation approach for programming online judges supported by data preprocessing techniques数据预处理技术支持的在线法官编程推荐方法APPLIED INTELLIGENCE
APPLIED INTELLIGENCEIF3.5
PREAI
分享收藏An Efficient Multi-Label SVM Classification Algorithm by Combining Approximate Extreme Points Method and Divide-and-Conquer StrategyIEEE ACCESS
OAAI
IEEE ACCESSIF3.6
OAAI分享收藏Changes in Milk pH and Bicarbonate During 20 Days of Lactation in the Guinea Pig豚鼠泌乳期20天内的乳pH值和碳酸氢盐变化OAAI
OAAI分享收藏
查看更多内容