针对编译错误生成目标示例
Targeted Example Generation for Compilation Errors
摘要：
我们提出TEGCER，一个自动反馈工具，为新手程序员。TEGCER使用监督分类将新代码提交中的编译错误与其他学生之前提交的相关预先存在的错误相匹配。用于执行此分类任务的密集神经网络在15000多个错误修复代码示例上进行训练。该模型在212个错误类别标签中的测试集分类Pred@3准确率为97.7%。使用这个模型作为基础，TEGCER为学生提供了最相关的解决方案的例子，他们的特定错误的需求。
英文摘要：
We present TEGCER, an automated feedback tool for novice programmers. TEGCER uses supervised classification to match compilation errors in new code submissions with relevant pre-existing errors, submitted by other students before. The dense neural network used to perform this classification task is trained on 15000+ error-repair code examples. The proposed model yields a test set classification Pred@3 accuracy of 97.7% across 212 error category labels. Using this model as its base, TEGCER presents students with the closest relevant examples of solutions for their specific error on demand.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
