Papers › Mapping Language to Code in Programmatic Context

Mapping Language to Code in Programmatic Context

29 Aug 2018EMNLP 2018 10arXiv:1808.09588archive 2025-07-28

Srinivasan Iyer, Ioannis Konstas, Alvin Cheung, Luke Zettlemoyer

Source code is rarely written in isolation. It depends significantly on the programmatic context, such as the class that the code would reside in. To study this phenomenon, we introduce the task of generating class member functions given English documentation and the programmatic context provided by the rest of the class. This task is challenging because the desired code can vary greatly depending on the functionality the class provides (e.g., a sort function may or may not be available when we are asked to "return the smallest element" in a particular member variable list). We introduce CONCODE, a new large dataset with over 100,000 examples consisting of Java classes from online code repositories, and develop a new encoder-decoder architecture that models the interaction between the method documentation and the class environment. We also present a detailed error analysis suggesting that there is significant room for future work on this task.

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sequence_mask sriniiyer/concode/GlobalAttention.py official repository ran · our draft was wrong MIT (permissive) · 94671955165a864c · report
getRuleAtNode sriniiyer/concode/Tree.py official repository unverified MIT (permissive) · ce9bd249712992ab · report
nname sriniiyer/concode/Tree.py official repository unverified MIT (permissive) · dc8f5cc8a55d9ba3 · report

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