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
Libraries
Use these libraries to find Source Code Summarization models and implementationsDatasets
Most implemented papers
CoDesc: A Large Code-Description Parallel Dataset
In this study, we present CoDesc -- a large parallel dataset composed of 4. 2 million Java methods and natural language descriptions.
Code to Comment Translation: A Comparative Study on Model Effectiveness & Errors
Automated source code summarization is a popular software engineering research topic wherein machine translation models are employed to "translate" code snippets into relevant natural language descriptions.
On the Evaluation of Neural Code Summarization
To achieve a profound understanding of how far we are from solving this problem and provide suggestions to future research, in this paper, we conduct a systematic and in-depth analysis of 5 state-of-the-art neural code summarization models on 6 widely used BLEU variants, 4 pre-processing operations and their combinations, and 3 widely used datasets.
GraphSearchNet: Enhancing GNNs via Capturing Global Dependencies for Semantic Code Search
Specifically, we propose to construct graphs for the source code and queries with bidirectional GGNN (BiGGNN) to capture the local structural information of the source code and queries.
Modeling Hierarchical Syntax Structure with Triplet Position for Source Code Summarization
In this paper, we propose CODESCRIBE to model the hierarchical syntax structure of code by introducing a novel triplet position for code summarization.
Leveraging Unsupervised Learning to Summarize APIs Discussed in Stack Overflow
Automated source code summarization is a task that generates summarized information about the purpose, usage, and--or implementation of methods and classes to support understanding of these code entities.
Assemble Foundation Models for Automatic Code Summarization
Thereby, we propose a flexible and robust approach for automatic code summarization, based on neural models.
Compositionality-Aware Graph2Seq Learning
It is expected that the compositionality in a graph can be associated to the compositionality in the output sequence in many graph2seq tasks.
Source Code Summarization with Structural Relative Position Guided Transformer
We further show that how the proposed SCRIPT captures the structural relative dependencies.
M2TS: Multi-Scale Multi-Modal Approach Based on Transformer for Source Code Summarization
They use the learned code representations as input to code summarization models, which can accordingly generate summaries describing source code.