Rank Learning-Based Code Readability Assessment with Siamese Neural Networks

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%.

BibTeX

@inproceedings{Mi:ASE22,
  author    = {Qing Mi},
  title     = {Rank {Learning-Based} Code Readability Assessment with Siamese Neural Networks},
  booktitle = {ASE},
  pages     = {208:1--208:2},
  publisher = {{ACM}},
  year      = {2022},
}

Rank Learning-Based Code Readability Assessment with Siamese Neural Networks

Abstract

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