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Found 7 papers, 5 papers with code
EyeTrans: Merging Human and Machine Attention for Neural Code Summarization
Neural code summarization leverages deep learning models to automatically generate brief natural language summaries of code snippets.
Revisiting File Context for Source Code Summarization
Source code summarization is the task of writing natural language descriptions of source code.
Statement-based Memory for Neural Source Code Summarization
For example, by taking the entire subroutine as input to a Transformer or RNN-based encoder.
Towards Modeling Human Attention from Eye Movements for Neural Source Code Summarization
The attention mechanism learns to connect features in source code to specific words to use when generating natural language descriptions.
A Language Model of Java Methods with Train/Test Deduplication
In contrast to many existing language models, we prioritize features for researchers including an open and easily-searchable training set, a held out test set with different levels of deduplication from the training set, infrastructure for deduplicating new examples, and an implementation platform suitable for execution on equipment accessible to a relatively modest budget.
Project-Level Encoding for Neural Source Code Summarization of Subroutines
Source code summarization of a subroutine is the task of writing a short, natural language description of that subroutine.
A Neural Question Answering System for Basic Questions about Subroutines
In this paper, we take initial steps to bringing state-of-the-art neural QA technologies to Software Engineering applications by designing a context-based QA system for basic questions about subroutines.
