Abstract
In recent years, deep learning is increasingly prevalent in the field of Software Engineering (SE). Especially, representation learning, which can learn vectors from the syntactic and semantics of the code, offers much convenience and promotion for the downstream tasks such as code search and vulnerability detection. In this work, we introduce our two applications of leveraging representation learning for software analysis, including defect prediction and vulnerability detection.
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CITATION STYLE
Fan, M., Jia, A., Liu, J., Liu, T., & Chen, W. (2020). When representation learning meets software analysis. In RL+SE and PL 2020 - Proceedings of the 1st ACM SIGSOFT International Workshop on Representation Learning for Software Engineering and Program Languages, Co-located with ESEC/FSE 2020 (pp. 17–18). Association for Computing Machinery, Inc. https://doi.org/10.1145/3416506.3423578
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