一句话：TEGCER 用监督分类将新手编译错误匹配到已有错误类别，并按需推荐相似修复示例。
论文针对新手编程时难以理解和修复编译错误的问题，提出自动化反馈工具 TEGCER。它利用监督分类，将新提交代码中的编译错误与以往学生提交的相关错误进行匹配；用于分类的稠密神经网络在 15000 多个错误-修复代码示例上训练，在 212 个错误类别标签上取得 97.7% 的 Pred@3 测试准确率。以该模型为基础，TEGCER 能在学生需要时展示与其具体错误最相关的解题示例。大规模可用性研究（N>230）表明，使用 TEGCER 的学生平均比接受人工辅导的学生快 25% 以上解决错误。
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. A large scale (N>230) usability study shows that students who use TEGCER are able to resolve errors more than 25% faster on average than students being assisted by human tutors.