Automatically detecting bugs in student program code is critical to enable formative feedback to help students pinpoint errors and resolve them. Deep learning models especially code2vec and ASTNN have shown great success for "large-scale" code classification. It is not clear, however, whether they can be effectively used for bug detection when the amount of labeled data is limited. In this work, we investigated the effectiveness of code2vec and ASTNN against classic machine learning models by varying the amount of labeled data from 1% up to 100%. With a few exceptions, the two deep learning models outperform the classic models. More interestingly, our results showed that when the amount of labeled data is small, code2vec is more effective, while ASTNN is more effective with more training data; for both code2vec and ASTNN, the more labeled data, the better. To further improve their effectiveness, we investigated the potential of semi-supervised learning which can leverage a large amount of unlabeled data to improve their performance. Our results showed that semi-supervised learning is indeed beneficial especially for ASTNN. [For the full proceedings, see ED615472.]