Source Code Summarization
37 papers with code • 9 benchmarks • 7 datasets
Code Summarization is a task that tries to comprehend code and automatically generate descriptions directly from the source code.
Source: Improving Automatic Source Code Summarization via Deep Reinforcement Learning
Benchmarks
These leaderboards are used to track progress in Source Code Summarization
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Libraries
Use these libraries to find Source Code Summarization models and implementationsDatasets
CodeSearchNet
StaQC
Latest papers with no code
Self-Supervised Contrastive Learning for Code Retrieval and Summarization via Semantic-Preserving Transformations
Corder is designed to alleviate the need of labeled data for code retrieval and code summarization tasks.
A Structural Transformer with Relative Positions in Trees for Code-to-Sequence Tasks
We suggest two approaches to incorporate syntactic information into transformer models encoding trees (e. g. abstract syntax trees) and generating sequences.
Leveraging Code Generation to Improve Code Retrieval and Summarization via Dual Learning
Since both tasks aim to model the association between natural language and programming language, recent studies have combined these two tasks to improve their performance.
A Convolutional Neural Network for Language-Agnostic Source Code Summarization
Automatic source code summarization may therefore have the ability to significantly improve the software development process.
Learning to Mine Aligned Code and Natural Language Pairs from Stack Overflow
For tasks like code synthesis from natural language, code retrieval, and code summarization, data-driven models have shown great promise.
