arXiv:1909.00769
공식 코드 · Papers with Code
Targeted Example Generation for Compilation Errors
원문 초록
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.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Generative Adversarial Minority Oversampling for Spectral-Spatial Hyperspectral Image Classification
Attention-Based Adaptive Spectral-Spatial Kernel ResNet for Hyperspectral Image Classification
Image and Text fusion for UPMC Food-101 \\using BERT and CNNs
Kidney Level Lupus Nephritis Classification using Uncertainty Guided Bayesian Convolutional Neural Networks
Beyond the Attention: Distinguish the Discriminative and Confusable Features For Fine-grained Image Classification
Orthrus: A Bimodal Learning Architecture for Malware Classification
토론
이 논문과 연결된 포럼 글이 아직 없습니다.