Cited by 79
The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
A general path-based representation for predicting program properties
Alon, Uri, Zilberstein, Meital, Levy, Omer, Yahav, Eran·2018·Proceedings of the 39th ACM SIGPLAN Conference on Programming Language Design and Implementation
Abstract
Predicting program properties such as names or expression types has a wide range of applications. It can ease the task of programming, and increase programmer productivity. A major challenge when learning from programs is how to represent programs in a way that facilitates effective learning.
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Crossref · 10.1145/3192366.3192412
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2 of 2 references verified0 citation errors · 0 not checkable
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Code-DKT's code extraction component is based on code2vec but adds score to the attention mechanism to assign weights to code paths for predictions.
“Code-DKT's code extraction component is based on the code2vec model, but adds score to the attention mechanism to assign weights to code paths [4] for predictions.”
From Code-DKT: A Code-based Knowledge Tracing Model for Programming Tasks (2022) · cites this paper as [4]
Code-DKT uses the code2vec model to learn a representation of student code and combines it with DKT to track student progress.
“we introduce a novel code-based deep knowledge tracing (Code-DKT) model, which uses the code2vec model [4] to learn a meaningful representation of student code, and combines this with Deep Knowledge Tracing (DKT) [34] to track student progress.”
From Code-DKT: A Code-based Knowledge Tracing Model for Programming Tasks (2022) · cites this paper as [4]
Citations
Cites
node2vec: Scalable Feature Learning for Networks
Grover, A., & Leskovec, J. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939754 · Cited by 10,926
Understanding Machine Learning: From Theory to Algorithms
Shalev-Shwartz, S., & Ben-David, S. (2014) · doi:10.1017/cbo9781107298019 · Cited by 3,115
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Predictive Comment Updating With Heuristics and AST-Path-Based Neural Learning: A Two-Phase Approach
Lin, B., Wang, S., Liu, Z., Xia, X., & Mao, X. (2023) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2022.3185458 · Cited by 27
Code-DKT: A Code-based Knowledge Tracing Model for Programming Tasks
Shi, Y., Chi, M., Barnes, T., & Price, T. (2022) · Zenodo · doi:10.5281/zenodo.6853105 · Cited by 7
Metadata
- Authors
- Alon, U., Zilberstein, M., Levy, O., & Yahav, E.
- Affiliations
- Technion, Israel
- University of Washington, USA
- Venue
- Proceedings of the 39th ACM SIGPLAN Conference on Programming Language Design and Implementation
- Publisher
- ACM
- Published
- 2018-06-11
- Pages
- 404-419
- Cited by
- 79
- References
- 2
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