Testing Neural Program Analyzers

K. Wang, “Learning scalable and precise representation of program semantics,” CoRR , vol. abs/1905.05251, 2019. [Online]. Available: http://arxiv.org/abs/1905.05251

Original

1905

Earlier work this paper cites.

K. Wang and M. Christodorescu, “COSET: A benchmark for evaluating neural program embeddings,” CoRR , vol. abs/1905.11445, 2019. [Online]. Available: http://arxiv.org/abs/1905.11445

Original

1905

Earlier work this paper cites.

K. S. Tai, R. Socher, and C. D. Manning, “Improved semantic representations from tree-structured long short-term memory networks,” CoRR , vol. abs/1503.00075, 2015. [Online]. Available: http://arxiv.org/abs/1503.00075

Original

2015

Earlier work this paper cites.

M. Allamanis, M. Brockschmidt, and M. Khademi, “Learning to represent programs with graphs,” CoRR , vol. abs/1711.00740, 2017. [Online]. Available: http://arxiv.org/abs/1711.00740

Original

2017

Earlier work this paper cites.

K. Wang, R. Singh, and Z. Su, “Dynamic neural program embedding for program repair,” CoRR , vol. abs/1711.07163, 2017. [Online]. Available: http://arxiv.org/abs/1711.07163

Original

2017

Earlier work this paper cites.

X. Xu, C. Liu, Q. Feng, H. Yin, L. Song, and D. Song, “Neural network-based graph embedding for cross-platform binary code similarity detection,” CoRR , vol. abs/1708.06525, 2017. [Online]. Available: http://arxiv.org/abs/1708.06525

Original

2017

Earlier work this paper cites.

U. Alon, O. Levy, and E. Yahav, “code2seq: Generating sequences from structured representations of code,” CoRR , vol. abs/1808.01400, 2018. [Online]. Available: http://arxiv.org/abs/1808.01400

Original

2018

Cited alongside, same era.

U. Alon, M. Zilberstein, O. Levy, and E. Yahav, “code2vec: Learning distributed representations of code,” CoRR , vol. abs/1803.09473, 2018. [Online]. Available: http://arxiv.org/abs/1803.09473

Original

2018

Cited alongside, same era.

J. Henkel, S. Lahiri, B. Liblit, and T. W. Reps, “Code vectors: Understanding programs through embedded abstracted symbolic traces,” CoRR , vol. abs/1803.06686, 2018. [Online]. Available: http://arxiv.org/abs/1803.06686

Original

2018

Cited alongside, same era.

Q. Lei, L. Wu, P. Chen, A. G. Dimakis, I. S. Dhillon, and M. Witbrock, “Discrete attacks and submodular optimization with applications to text classification,” CoRR , vol. abs/1812.00151, 2018. [Online]. Available: http://arxiv.org/abs/1812.00151

Original

2018

Cited alongside, same era.

B. Rychalska, D. Basaj, P. Biecek, and A. Wróblewska, “Does it care what you asked? understanding importance of verbs in deep learning QA system,” CoRR , vol. abs/1809.03740, 2018. [Online]. Available: http://arxiv.org/abs/1809.03740

Original

2018

Later among the works it cites.

Y. Tian, K. Pei, S. Jana, and B. Ray, “Deeptest: Automated testing of deep-neural-network-driven autonomous cars,” in Proceedings of the 40th International Conference on Software Engineering , ser. ICSE ’18. New York, NY, USA: ACM, 2018, pp. 303–314. [Online]. Available: http://doi.acm.org/10.1145/3180155.3180220

2018

Later among the works it cites.

M. Zhang, Y. Zhang, L. Zhang, C. Liu, and S. Khurshid, “Deeproad: Gan-based metamorphic testing and input validation framework for autonomous driving systems,” in Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering , ser. ASE 2018. New York, NY, USA: ACM, 2018, pp. 132–142. [Online]. Available: http://doi.acm.org/10.1145/3238147.3238187

Original

2018

Later among the works it cites.