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Found 18 papers, 14 papers with code
Do Machines and Humans Focus on Similar Code? Exploring Explainability of Large Language Models in Code Summarization
Furthermore, alignment between model and human foci in this setting does not seem to dictate the quality of the LLM-generated summaries.
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.
Distilled GPT for Source Code Summarization
A code summary is a brief natural language description of source code.
Semantic Similarity Loss for Neural Source Code Summarization
We also propose to combine our loss with traditional CCE for each word, which streamlines the training process compared to baselines.
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.
Generating Clarifying Questions for Query Refinement in Source Code Search
In source code search, a common information-seeking strategy involves providing a short initial query with a broad meaning, and then iteratively refining the query using terms gleaned from the results of subsequent searches.
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.
Improved Automatic Summarization of Subroutines via Attention to File Context
In this paper, we present an approach that models the file context of subroutines (i. e. other subroutines in the same file) and uses an attention mechanism to find words and concepts to use in summaries.
Software Engineering
Improved Code Summarization via a Graph Neural Network
The first approaches to use structural information flattened the AST into a sequence.
Recommendations for Datasets for Source Code Summarization
The main use for these descriptions is in software documentation e. g. the one-sentence Java method descriptions in JavaDocs.
A Neural Model for Generating Natural Language Summaries of Program Subroutines
In this paper, we present a neural model that combines words from code with code structure from an AST.
Software Engineering
Detecting Speech Act Types in Developer Question/Answer Conversations During Bug Repair
This paper targets the problem of speech act detection in conversations about bug repair.
Adapting Neural Text Classification for Improved Software Categorization
Software Categorization is the task of organizing software into groups that broadly describe the behavior of the software, such as "editors" or "science."
Automatically Generating Commit Messages from Diffs using Neural Machine Translation
We trained an NMT algorithm using a corpus of diffs and human-written commit messages from the top 1k Github projects.
