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The automated generation of test code can reduce the time and effort required to build software while increasing its correctness and robustness.
C. Csallner and Y. Smaragdakis, “JCrasher: an automatic robustness tester for Java,” Software: Practice and Experience , vol. 34, no. 11, pp. 1025–1050, sep 2004. [Online]. Available: http://doi.wiley.com/10.1002/spe.602
2004
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P. Tonella, “Evolutionary testing of classes,” ACM SIGSOFT Software Engineering Notes , vol. 29, no. 4, p. 119, jul 2004. [Online]. Available: http://portal.acm.org/citation.cfm?doid=1013886.1007528
2004
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K. Sen and G. Agha, “CUTE and jCUTE: Concolic Unit Testing and Explicit Path Model-Checking Tools,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) , 2006, vol. 4144 LNCS, pp. 419–423. [Online]. Available: http://link.springer.com/10.1007/11817963_38
2006
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C. Pacheco and M. D. Ernst, “Randoop: feedback-directed random testing for Java,” in Companion to the 22nd ACM SIGPLAN conference on Object oriented programming systems and applications companion - OOPSLA ’07 , vol. 2. New York, New York, USA: ACM Press, 2007, p. 815. [Online]. Available: http://portal.acm.org/citation.cfm?doid=1297846.1297902
2007
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J. H. Andrews, F. C. H. Li, and T. Menzies, “Nighthawk: A Two-Level Genetic-Random Unit Test Data Generator,” in Proceedings of the twenty-second IEEE/ACM international conference on Automated software engineering - ASE ’07 . New York, New York, USA: ACM Press, 2007, p. 144. [Online]. Available: http://portal.acm.org/citation.cfm?doid=1321631.1321654
2007
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B. V. Rompaey and S. Demeyer, “Establishing Traceability Links between Unit Test Cases and Units under Test,” in 2009 13th European Conference on Software Maintenance and Reengineering , no. ii. IEEE, 2009, pp. 209–218. [Online]. Available: http://ieeexplore.ieee.org/document/4812754/
2009
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C. S. Păsăreanu and N. Rungta, “Symbolic PathFinder: Symbolic Execution of Java Bytecode,” in Proceedings of the IEEE/ACM international conference on Automated software engineering - ASE ’10 , vol. 2. New York, New York, USA: ACM Press, 2010, p. 179. [Online]. Available: http://portal.acm.org/citation.cfm?doid=1858996.1859035
2010
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S. Park, B. M. M. Hossain, I. Hussain, C. Csallner, M. Grechanik, K. Taneja, C. Fu, and Q. Xie, “CarFast: Achieving Higher Statement Coverage Faster Sangmin,” in Proceedings of the ACM SIGSOFT 20th International Symposium on the Foundations of Software Engineering - FSE ’12 . New York, New York, USA: ACM Press, 2012, p. 1. [Online]. Available: http://dl.acm.org/citation.cfm?doid=2393596.2393636
2012
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G. Fraser and A. Arcuri, “Whole Test Suite Generation,” IEEE Transactions on Software Engineering , vol. 39, no. 2, pp. 276–291, feb 2013. [Online]. Available: http://ieeexplore.ieee.org/document/6152257/
2013
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N. Kalchbrenner and P. Blunsom, “Recurrent continuous translation models,” in Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing , 2013, pp. 1700–1709
2013
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R. Just, D. Jalali, and M. D. Ernst, “Defects4J: a database of existing faults to enable controlled testing studies for Java programs,” in Proceedings of the 2014 International Symposium on Software Testing and Analysis - ISSTA 2014 . New York, New York, USA: ACM Press, 2014, pp. 437–440. [Online]. Available: http://dl.acm.org/citation.cfm?doid=2610384.2628055
2014
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2014
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2014
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2014
Cited alongside, same era.
C. Klammer and A. Kern, “Writing unit tests: It’s now or never!” in 2015 IEEE Eighth International Conference on Software Testing, Verification and Validation Workshops (ICSTW) . IEEE, apr 2015, pp. 1–4. [Online]. Available: http://ieeexplore.ieee.org/document/7107469/
2015
Cited alongside, same era.
S. Shamshiri, “Automated unit test generation for evolving software,” 2015 10th Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering, ESEC/FSE 2015 - Proceedings , pp. 1038–1041, 2015
2015
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2018
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J. Henkel, S. K. Lahiri, B. Liblit, and T. Reps, “Code vectors: understanding programs through embedded abstracted symbolic traces,” in Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering - ESEC/FSE 2018 . New York, New York, USA: ACM Press, 2018, pp. 163–174. [Online]. Available: http://dl.acm.org/citation.cfm?doid=3236024.3236085
2018
Later among the works it cites.
G. Zhao and J. Huang, “DeepSim: deep learning code functional similarity,” in Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering - ESEC/FSE 2018 . New York, New York, USA: ACM Press, 2018, pp. 141–151. [Online]. Available: http://dl.acm.org/citation.cfm?doid=3236024.3236068
2018
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2015
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2015
Cited alongside, same era.
M. White, M. Tufano, C. Vendome, and D. Poshyvanyk, “Deep learning code fragments for code clone detection,” in Proceedings of the 31st IEEE/ACM International Conference on Automated Software Engineering - ASE 2016 . New York, New York, USA: ACM Press, 2016, pp. 87–98. [Online]. Available: http://dl.acm.org/citation.cfm?doid=2970276.2970326
2016
Cited alongside, same era.
2016
Cited alongside, same era.
J. Gu, Z. Lu, H. Li, and V. O. Li, “Incorporating Copying Mechanism in Sequence-to-Sequence Learning,” in Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , vol. 3. Stroudsburg, PA, USA: Association for Computational Linguistics, 2016, pp. 1631–1640. [Online]. Available: http://aclweb.org/anthology/P16-1154
2016
Cited alongside, same era.
R. Pawlak, M. Monperrus, N. Petitprez, C. Noguera, and L. Seinturier, “Spoon: A library for implementing analyses and transformations of java source code,” Software: Practice and Experience , vol. 46, no. 9, pp. 1155–1179, 2016
2016
Cited alongside, same era.
J. Guo, J. Cheng, and J. Cleland-Huang, “Semantically Enhanced Software Traceability Using Deep Learning Techniques,” in 2017 IEEE/ACM 39th International Conference on Software Engineering (ICSE) . IEEE, may 2017, pp. 3–14. [Online]. Available: http://ieeexplore.ieee.org/document/7985645/
2017
Cited alongside, same era.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention Is All You Need,” Behavioral and Brain Sciences , vol. 40, no. Nips, p. e253, jun 2017. [Online]. Available: http://papers.nips.cc/paper/7181-attention-is-all-you-need
2017
Cited alongside, same era.
2017
Cited alongside, same era.
X. Hu, G. Li, X. Xia, D. Lo, and Z. Jin, “Deep code comment generation,” in 2018 IEEE/ACM 26th International Conference on Program Comprehension (ICPC) . IEEE, 2018, pp. 200–20 010
2018
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V. J. Hellendoorn, C. Bird, E. T. Barr, and M. Allamanis, “Deep learning type inference,” in Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering - ESEC/FSE 2018 , vol. 18. New York, New York, USA: ACM Press, 2018, pp. 152–162. [Online]. Available: https://doi.org/10.1145/3236024.3236051 http://dl.acm.org/citation.cfm?doid=3236024.3236051
2018
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U. Alon, M. Zilberstein, O. Levy, and E. Yahav, “code2vec: Learning distributed representations of code,” Proceedings of the ACM on Programming Languages , vol. 3, no. POPL, pp. 1–29, 2019
2019
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C. Watson, M. Tufano, K. Moran, G. Bavota, and D. Poshyvanyk, “On Learning Meaningful Assert Statements for Unit Test Cases,” in 42nd International Conference on Software Engineering (ICSE ’20), May 23–29, 2020, Seoul, Republic of Korea , 2020
2020
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2020
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R. White, J. Krinke, and R. Tan, “Establishing Multilevel Test-to-Code Traceability Links,” in 42nd International Conference on Software Engineering (ICSE ’20) . Seoul, Republic of Korea: ACM, 2020. [Online]. Available: https://doi.org/10.1145/3377811.3380921
2020
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Google, “google/trax,” 2020. [Online]. Available: https://github.com/google/trax
2020
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S. Iyer, I. Konstas, A. Cheung, and L. Zettlemoyer, “Summarizing source code using a neural attention model,” in Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , 2016, pp. 2073–2083
2083
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M. Allamanis, H. Peng, and C. Sutton, “A convolutional attention network for extreme summarization of source code,” in International conference on machine learning , 2016, pp. 2091–2100
2091
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