@article{Jinpa_Gao_2022, title={Code Representation Learning Using Prüfer Sequences (Student Abstract)}, volume={36}, url={https://ojs.aaai.org/index.php/AAAI/article/view/21625}, DOI={10.1609/aaai.v36i11.21625}, abstractNote={An effective and efficient encoding of the source code of a computer program is critical to the success of sequence-to-sequence deep neural network models for code representation learning. In this study, we propose to use the Prufer sequence of the Abstract Syntax Tree (AST) of a computer program to design a sequential representation scheme that preserves the structural information in an AST. Our representation makes it possible to develop deep-learning models in which signals carried by lexical tokens in the training examples can be exploited automatically and selectively based on their syntactic role and importance. Unlike other recently-proposed approaches, our representation is concise and lossless in terms of the structural information of the AST. Results from our experiment show that prufer-sequence-based representation is indeed highly effective and efficient.}, number={11}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Jinpa, Tenzin and Gao, Yong}, year={2022}, month={Jun.}, pages={12977-12978} }