Retrieval-based neural source code summarization

Zhang, Jian; Wang, Xu; Zhang, Hongyu; Sun, Hailong; Li, Xudong

doi:10.1145/3377811.3380383

Cited by 206 publications

(147 citation statements)

References 49 publications

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“…Among other noteworthy works, API usage information (Hu et al, 2018b), reinforcement learning (Wan et al, 2018), dual learning , retrieval-based techniques (Zhang et al, 2020) are leveraged to further enhance the code summarization models. We can enhance a Transformer with previously proposed techniques; however, in this work, we limit ourselves to study different design choices for a Transformer without breaking its' core architectural design philosophy.…”

Section: Related Workmentioning

confidence: 99%

A Transformer-based Approach for Source Code Summarization

Ahmad

Chakraborty

Ray

et al. 2020

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

278

185

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Generating a readable summary that describes the functionality of a program is known as source code summarization. In this task, learning code representation by modeling the pairwise relationship between code tokens to capture their long-range dependencies is crucial. To learn code representation for summarization, we explore the Transformer model that uses a self-attention mechanism and has shown to be effective in capturing long-range dependencies. In this work, we show that despite the approach is simple, it outperforms the state-of-the-art techniques by a significant margin. We perform extensive analysis and ablation studies that reveal several important findings, e.g., the absolute encoding of source code tokens' position hinders, while relative encoding significantly improves the summarization performance. We have made our code publicly available 1 to facilitate future research.

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Section: Related Workmentioning

confidence: 99%

A Transformer-based Approach for Source Code Summarization

Ahmad

Chakraborty

Ray

et al. 2020

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

278

185

View full text Add to dashboard Cite

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“…The data augmentation method used in NFR, which was inspired by work conducted in automated post-editing [30] and multi-source translation [31], has been shown to also yield promising results in other fields, such as text generation [32] and code summarization [33]. In the context of MT, it has also proven to be helpful in domain adaptation [5], increasing data robustness [34] and specialised translation tasks [35].…”

Section: Tm-mt Integrationmentioning

confidence: 99%

Evaluating the Impact of Integrating Similar Translations into Neural Machine Translation

Tezcan

Bulté

2022

Information

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Previous research has shown that simple methods of augmenting machine translation training data and input sentences with translations of similar sentences (or fuzzy matches), retrieved from a translation memory or bilingual corpus, lead to considerable improvements in translation quality, as assessed by a limited set of automatic evaluation metrics. In this study, we extend this evaluation by calculating a wider range of automated quality metrics that tap into different aspects of translation quality and by performing manual MT error analysis. Moreover, we investigate in more detail how fuzzy matches influence translations and where potential quality improvements could still be made by carrying out a series of quantitative analyses that focus on different characteristics of the retrieved fuzzy matches. The automated evaluation shows that the quality of NFR translations is higher than the NMT baseline in terms of all metrics. However, the manual error analysis did not reveal a difference between the two systems in terms of total number of translation errors; yet, different profiles emerged when considering the types of errors made. Finally, in our analysis of how fuzzy matches influence NFR translations, we identified a number of features that could be used to improve the selection of fuzzy matches for NFR data augmentation.

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“…Zhang et al [25] tried to handle the words with low frequency in the dataset. They suggested a source code summarization system that integrated retrieval-based methods with NMT-based methods.…”

Section: Related Workmentioning

confidence: 99%

Retrieval-Based Transformer Pseudocode Generation

et al. 2022

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The comprehension of source code is very difficult, especially if the programmer is not familiar with the programming language. Pseudocode explains and describes code contents that are based on the semantic analysis and understanding of the source code. In this paper, a novel retrieval-based transformer pseudocode generation model is proposed. The proposed model adopts different retrieval similarity methods and neural machine translation to generate pseudocode. The proposed model handles words of low frequency and words that do not exist in the training dataset. It consists of three steps. First, we retrieve the sentences that are similar to the input sentence using different similarity methods. Second, pass the source code retrieved (input retrieved) to the deep learning model based on the transformer to generate the pseudocode retrieved. Third, the replacement process is performed to obtain the target pseudo code. The proposed model is evaluated using Django and SPoC datasets. The experiments show promising performance results compared to other language models of machine translation. It reaches 61.96 and 50.28 in terms of BLEU performance measures for Django and SPoC, respectively.

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Retrieval-based neural source code summarization

Cited by 206 publications

References 49 publications

A Transformer-based Approach for Source Code Summarization

A Transformer-based Approach for Source Code Summarization

Evaluating the Impact of Integrating Similar Translations into Neural Machine Translation

Retrieval-Based Transformer Pseudocode Generation

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