Icon2Code: Recommending code implementations for Android GUI components

Zhao, Yanjie; Li, Li; Sun, Xiaoyu; Liu, Pei; Grundy, John

doi:10.1016/j.infsof.2021.106619

Cited by 7 publications

(2 citation statements)

References 23 publications

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“…To this end, more and more researchers have started to train the learning models with code's structural information, which can be obtained from the code's AST 16,17,[19][20][21][22]25,[31][32][33][34][35] and the Application Programming Interface (API) sequence. 13,36,37 For example, Wan et al 21 put forward a framework based on reinforcement learning to automatically summarize code snippets. Their framework respectively encodes the AST structure as well as the plain code sequence via AST-based LSTM and LSTM, which are integrated together by a hybrid attention layer.…”

Section: Related Workmentioning

confidence: 99%

“…Consequently, the power of the corresponding code‐to‐text models may be affected. To this end, more and more researchers have started to train the learning models with code's structural information, which can be obtained from the code's AST 16,17,19–22,25,31–35 and the Application Programming Interface (API) sequence 13,36,37 . For example, Wan et al 21 put forward a framework based on reinforcement learning to automatically summarize code snippets.…”

Section: Related Workmentioning

confidence: 99%

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PyScribe–Learning to describe python code

Guo,

Liu,

Liu

et al. 2023

Softw Pract Exp

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Code comment generation, which attempts to summarize the functionality of source code in textual descriptions, plays an important role in automatic software development research. Currently, several structural neural networks have been exploited to preserve the syntax structure of source code based on abstract syntax trees (ASTs). However, they can not well capture both the long‐distance and local relations between nodes while retaining the overall structural information of AST. To mitigate this problem, we present a prototype tool titled PyScribe, which extends the Transformer model to a new encoder‐decoder‐based framework. Particularly, the triplet position is designed and integrated into the node‐level and edge‐level structural features of AST for producing Python code comments automatically. This paper, to the best of our knowledge, makes the first effort to model the edges of AST as an explicit component for improved code representation. By specifying triplet positions for each node and edge, the overall structural information can be well preserved in the learning process. Moreover, the captured node and edge features go through a two‐stage decoding process to yield higher qualified comments. To evaluate the effectiveness of PyScribe, we resort to a large dataset of code‐comment pairs by mining Jupyter Notebooks from GitHub, for which we have made it publicly available to support further studies. The experimental results reveal that PyScribe is indeed effective, outperforming the state‐ofthe‐art by achieving an average BLEU score (i.e., av‐BLEU) of 0.28.

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