Learning Based Methods for Code Runtime Complexity Prediction

Author:

Sikka Jagriti,Satya Kushal,Kumar Yaman,Uppal Shagun,Shah Rajiv Ratn,Zimmermann Roger

Publisher

Springer International Publishing

Reference17 articles.

1. Are runtime bounds in p decidable? (answer: no). https://cstheory.stackexchange.com/questions/5004/are-runtime-bounds-in-p-decidable-answer-no

2. Graph2vec implementation. https://github.com/MLDroid/graph2vec_tf

3. Allamanis, M., Peng, H., Sutton, C.: A convolutional attention network for extreme summarization of source code. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of The 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, PMLR, New York, New York, USA, 20–22 June 2016, vol. 48, pp. 2091–2100. http://proceedings.mlr.press/v48/allamanis16.html

4. Alon, U., Zilberstein, M., Levy, O., Yahav, E.: A general path-based representation for predicting program properties. CoRR abs/1803.09544 (2018). http://arxiv.org/abs/1803.09544

5. Alon, U., Zilberstein, M., Levy, O., Yahav, E.: Code2vec: learning distributed representations of code. Proc. ACM Program. Lang. 3(POPL), 40:1–40:29 (2019). https://doi.org/10.1145/3290353

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