Abstract
Code generation is a technique that generates program source code without human intervention. There has been much research on automated methods for writing code, such as code generation. However, many techniques are still in their infancy and often generate syntactically incorrect code. Therefore, automated metrics used in natural language processing (NLP) are occasionally used to evaluate existing techniques in code generation. At present, it is unclear which metrics in NLP are more suitable than others for evaluating generated codes. In this study, we clarify which NLP metrics are applicable to syntactically incorrect code and suitable for the evaluation of techniques that automatically generate codes. Our results show that METEOR is the best of the automated metrics compared in this study.
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References
Ahmad, W., Chakraborty, S., Ray, B., Chang, K.W.: Unified pre-training for program understanding and generation. In: Proceedings of Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (2021)
Banerjee, S., Lavie, A.: METEOR: An automatic metric for MT evaluation with improved correlation with human judgments. In: Proceedings of ACL Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization (2005)
Denkowski, M., Lavie, A.: Meteor universal: language specific translation evaluation for any target language. In: Proceedings of Workshop on Statistical Machine Translation (2014)
Dong, L., Lapata, M.: Coarse-to-Fine decoding for neural semantic parsing. In: Proceedings of Annual Meeting of the Association for Computational Linguistics (2018)
Karaivanov, S., Raychev, V., Vechev, M.: Phrase-based statistical translation of programming languages. In: Proceedings of ACM International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software (2014)
Lin, C.Y.: ROUGE: a package for automatic evaluation of summaries. In: Proceedings of ACL Text Summarization Branches Out (2004)
Liu, H., Shen, M., Zhu, J., Niu, N., Li, G., Zhang, L.: Deep learning based program generation from requirements text: are we there yet? IEEE Trans. Softw. Eng. 48(4), 1268–1289 (2022)
Papineni, K., Roukos, S., Ward, T., Zhu, W.J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of Annual Meeting of the Association for Computational Linguistics (2002)
Parisotto, E., Mohamed, A., Singh, R., Li, L., Zhou, D., Kohli, P.: Neuro-symbolic program synthesis. In: Proceedings of International Conference on Learning Representations (2017)
Rabinovich, M., Stern, M., Klein, D.: Abstract syntax networks for code generation and semantic parsing (2017). https://arxiv.org/abs/1704.07535
Spector, L.: Autoconstructive evolution: Push, PushGP, and Pushpop. In: Proceedings of Genetic and Evolutionary Computation Conference (2001)
Svyatkovskiy, A., Deng, S.K., Fu, S., Sundaresan, N.: Intellicode compose: code generation using transformer. In: Proceedings of ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (2020)
Tran, N., Tran, H., Nguyen, S., Nguyen, H., Nguyen, T.: Does BLEU score work for code migration? In: Proceedings of IEEE/ACM International Conference on Program Comprehension (2019)
Yin, P., Neubig, G.: A syntactic neural model for general-purpose code generation. In: Proceedings of Annual Meeting of the Association for Computational Linguistics (2017)
Zhao, G., Huang, J.: Deepsim: deep learning code functional similarity. In: Proceedings of ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (2018)
Acknowledgements
This research was supported by JSPS KAKENHI, Japan (grant numbers JP20H04166, JP21K18302, JP21K11820, JP21H04877, JP22H03567, and JP22K11985).
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Takaichi, R. et al. (2022). Are NLP Metrics Suitable for Evaluating Generated Code?. In: Taibi, D., Kuhrmann, M., Mikkonen, T., Klünder, J., Abrahamsson, P. (eds) Product-Focused Software Process Improvement. PROFES 2022. Lecture Notes in Computer Science, vol 13709. Springer, Cham. https://doi.org/10.1007/978-3-031-21388-5_38
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DOI: https://doi.org/10.1007/978-3-031-21388-5_38
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