Rank Learning-Based Code Readability Assessment with Siamese Neural Networks
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Qing MiAuthors Info & Claims
Qing MiAuthors Info & ClaimsASE '22: Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering
Article No.: 208, Pages 1 - 2
Abstract
Automatically assessing code readability is a relatively new challenge that has attracted growing attention from the software engineering community. In this paper, we outline the idea to regard code readability assessment as a learning-to-rank task. Specifically, we design a pairwise ranking model with siamese neural networks, which takes as input a code pair and outputs their readability ranking order. We have evaluated our approach on three publicly available datasets. The result is promising, with an accuracy of 83.5%, a precision of 86.1%, a recall of 81.6%, an F-measure of 83.6% and an AUC of 83.4%.
References
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Raymond P L Buse and Westley R. Weimer. 2010. Learning a Metric for Code Readability. IEEE Transactions on Software Engineering 36, 4 (jul 2010), 546–558. https://doi.org/10.1109/TSE.2009.70
[2]
Jonathan Dorn. 2012. A General Software Readability Model. University of Virginia, Charlottesville, Virginia (2012), 1–62. http://cheetah.cs.virginia.edu/ weimer/students/dorn-mcs-pres.pdf
[3]
Qing Mi, Jacky Keung, Yan Xiao, Solomon Mensah, and Yujin Gao. 2018. Improving code readability classification using convolutional neural networks. Information and Software Technology 104, November 2017(2018), 60–71. https://doi.org/10.1016/j.infsof.2018.07.006
[4]
Valentina Piantadosi, Fabiana Fierro, Simone Scalabrino, Alexander Serebrenik, and Rocco Oliveto. 2020. How does code readability change during software evolution?Empirical Software Engineering 25, 6 (2020), 5374–5412.
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Simone Scalabrino, Mario Linares-Vásquez, Rocco Oliveto, and Denys Poshyvanyk. 2018. A comprehensive model for code readability. Journal of Software: Evolution and Process 30, 6 (jun 2018), e1958. https://doi.org/10.1002/smr.1958
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Eliane S. Wiese, Anna N. Rafferty, and Armando Fox. 2019. Linking code readability, structure, and comprehension among novices: It’s complicated. International Conference on Software Engineering: Software Engineering Education and Training(2019), 84–94. https://doi.org/10.1109/ICSE-SEET.2019.00017
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Published In
October 2022
2006 pages
ISBN:9781450394758
DOI:10.1145/3551349
Copyright © 2022 ACM.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 05 January 2023
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- Short-paper
- Research
- Refereed limited
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- GHfund B
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ASE '22
ASE '22: 37th IEEE/ACM International Conference on Automated Software Engineering
October 10 - 14, 2022
MI, Rochester, USA
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Overall Acceptance Rate 82 of 337 submissions, 24%
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