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Many automatic unit test generation tools that can generate unit test cases with high coverage over a program have been proposed.
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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, 2012
2012
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P. McMinn, M. Shahbaz, and M. Stevenson, “Search-based test input generation for string data types using the results of web queries,” in 2012 IEEE Fifth International Conference on Software Testing, Verification and Validation . IEEE, 2012, pp. 141–150
2012
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L. Buitinck, G. Louppe, M. Blondel, F. Pedregosa, A. Mueller, O. Grisel, V. Niculae, P. Prettenhofer, A. Gramfort, J. Grobler, R. Layton, J. VanderPlas, A. Joly, B. Holt, and G. Varoquaux, “API design for machine learning software: experiences from the scikit-learn project,” in ECML PKDD Workshop: Languages for Data Mining and Machine Learning , 2013, pp. 108–122
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2013
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W. Maalej and M. P. Robillard, “Patterns of knowledge in api reference documentation,” IEEE Transactions on Software Engineering , vol. 39, no. 9, pp. 1264–1282, 2013
2013
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M. Shahbaz, P. McMinn, and M. Stevenson, “Automatic generation of valid and invalid test data for string validation routines using web searches and regular expressions,” Science of Computer Programming , vol. 97, pp. 405–425, 2015
2015
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S. Shamshiri, R. Just, J. M. Rojas, G. Fraser, P. McMinn, and A. Arcuri, “Do automatically generated unit tests find real faults? an empirical study of effectiveness and challenges (t),” in ASE’15 , 2015, pp. 201–211
2015
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G. Fraser, M. Staats, P. McMinn, A. Arcuri, and F. Padberg, “Does automated unit test generation really help software testers? a controlled empirical study,” TOSEM’15 , vol. 24, no. 4, pp. 1–49, 2015
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2016
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2016
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2017
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Y. Hashemi, M. Nayebi, and G. Antoniol, “Documentation of machine learning software,” in 2020 IEEE 27th International Conference on Software Analysis, Evolution and Reengineering (SANER) , 2020, pp. 666–667
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2020
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S. Wang, N. Shrestha, A. K. Subburaman, J. Wang, M. Wei, and N. Nagappan, “Automatic unit test generation for machine learning libraries: How far are we?” in 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE) , 2021, pp. 1548–1560
2021
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A. Arcuri, J. P. Galeotti, B. Marculescu, and M. Zhang, “Evomaster: A search-based system test generation tool,” Journal of Open Source Software , vol. 6, no. 57, p. 2153, 2021
2021
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M. Liu, X. Peng, A. Marcus, C. Treude, X. Bai, G. Lyu, J. Xie, and X. Zhang, “Learning-based extraction of first-order logic representations of api directives,” in Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering , 2021, pp. 491–502
2021
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S. Nielebock, R. Heumüller, K. M. Schott, and F. Ortmeier, “Guided pattern mining for api misuse detection by change-based code analysis,” Automated Software Engineering , vol. 28, no. 2, pp. 1–48, 2021
2021
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2022
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D. Xie, Y. Li, M. Kim, H. V. Pham, L. Tan, X. Zhang, and M. W. Godfrey, “Docter: documentation-guided fuzzing for testing deep learning api functions,” in Proceedings of the 31st ACM SIGSOFT International Symposium on Software Testing and Analysis , 2022, pp. 176–188
2022
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Y. Deng, C. Yang, A. Wei, and L. Zhang, “Fuzzing deep-learning libraries via automated relational api inference,” in Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering , 2022, pp. 44–56
2022
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