Deep Code-Commend Understanding and Assessment

Deze Wang

doi:10.1109/ACCESS.2019.2957424

Deep Code-Commend Understanding and Assessment

2019-01-01

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DOI：10.1109/ACCESS.2019.2957424

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Code comments are a key software component for program comprehension and software maintainability. High-quality code and comments are urgently needed by data-driven models widely used in tasks like code summarization. Many existing approaches for assessing the quality of comments are machine learning based classification algorithms or rely on heuristic rules. These approaches are difficult to capture the complicated features of text data and are often limited in accuracy, efficiency, and generalization ability. In this paper, we convert the quality assessment of code comments into a classification problem based on the multi-input neural network. We summarize the input, the code and comments, into vectors using the attention-based Bi-LSTM model and the weighted GloVe model, respectively, and concatenate the code vectors and the comment vectors as the input of the Multiple-Layer Perceptron classifier for the comment quality assessment. Experimental results show that our approach, in general, outperforms the previous technique, on both our labeled dataset and the public dataset, with the Fl-score of 96.91% and 91.90%, respectively. Using the training set and the testing set from distinct sources, our approach can still achieve reasonable performance, which demonstrates its generalization ability.

Keywords:

Code comment

source code

multi-input neural network

text classification

Deep Code-Commend Understanding and Assessment

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