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An data augmentation method for source code summarization
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AI
DOI：10.1016/j.neucom.2023.126385.png)
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Abstract
En 中文
Code comments improve the readability and intelligibility of codes, Unfortunately, code comments are often missing, or outdated in software projects, which negatively affects the efficiency of developers to infer the functionality from source code and affect the efficiency of software maintenance and evolution. To solve this problem, many source code summarization algorithms have been proposed, which automatically generate code comments from source code. However, these methods usually try to collect a large data set which contains the mapping between code comments and source code to train models. However, there are two limitations for the training sets: the insufficient data collection limitation (i.e., generate a large amount of noises-free training data) and data distribution bias limitation (i.e., generate training data for infrequently used methods). To address this issues, we have proposed a data augmentation method for code comments, named CDA-CS. Training models on the augmented dataset, the state-of the-art algorithms can easily get a further 1.37% to 2.24% improvement in terms of different evaluation metrics (i.e., BLUE-4, METEOR, ROUGH_L).& COPY; 2023 Elsevier B.V. All rights reserved.
Keywords:
Code summary
Data augmentation
Clustering
Word replacement