A Convolutional Attention Network for Extreme Summarization of Source Code (1602.03001v2)

Abstract: Attention mechanisms in neural networks have proved useful for problems in which the input and output do not have fixed dimension. Often there exist features that are locally translation invariant and would be valuable for directing the model's attention, but previous attentional architectures are not constructed to learn such features specifically. We introduce an attentional neural network that employs convolution on the input tokens to detect local time-invariant and long-range topical attention features in a context-dependent way. We apply this architecture to the problem of extreme summarization of source code snippets into short, descriptive function name-like summaries. Using those features, the model sequentially generates a summary by marginalizing over two attention mechanisms: one that predicts the next summary token based on the attention weights of the input tokens and another that is able to copy a code token as-is directly into the summary. We demonstrate our convolutional attention neural network's performance on 10 popular Java projects showing that it achieves better performance compared to previous attentional mechanisms.

• The paper introduces a convolutional attention network that integrates convolution layers within the attention mechanism for summarizing code snippets.
• It employs a copy mechanism to handle out-of-vocabulary tokens, achieving superior F1 scores and exact match percentages over standard models.
• The approach enhances automated code documentation and refactoring, paving the way for advanced research in software comprehension.

Convolutional Attention Networks for Source Code Summarization

In the paper, the authors introduce a convolutional attention network designed for the task of extreme summarization of source code snippets. This work addresses the challenge of converting sequences of code tokens into summarized, descriptive method names — a task crucial for understanding and maintaining software systems.

Key Contributions

The authors propose a novel neural network architecture that integrates convolutional layers within the attention mechanism itself, enabling the model to detect both local and temporal translation-invariant features effectively. This architecture improves upon previous methods by focusing attention on relevant features throughout long and variable-length input sequences, aiding in the prediction of concise summaries for source code.

The model's ability to learn complex patterns and extract informative features provides significant improvements over traditional models, such as those used in natural language processing tasks like machine translation. The proposed convolutional attention model, complemented by a copy mechanism, achieves superior performance by allowing for direct copying of tokens from the input when predicting the output, addressing the common problem of out-of-vocabulary (OoV) tokens in software projects.

Numerical Results

The authors demonstrate the efficacy of their architecture through rigorous evaluations on ten popular open-source Java projects. The convolutional attention model with the copy mechanism outperformed traditional tf-idf approaches as well as standard attention models, achieving higher F1 scores and exact match percentages across projects.

Implications

Practically, the enhanced summarization capability of the model can aid developers in understanding and refactoring large codebases by auto-generating meaningful method names. Theoretically, this work suggests that convolutional mechanisms can be effectively integrated with attention models to handle long-sequence input data, broadening the scope of attention-based techniques in structured domains.

Future Developments

This work opens avenues for further research integrating convolutional configurations within attention frameworks in other domains, such as automated documentation generation and software comprehension tools. Advancements could focus on optimizing the convolutional filters for specific programming paradigms or exploring extensions to multi-modal code artifacts.

Overall, the convolutional attention network presented in this paper constitutes a significant advance in automated source code summarization, yielding insights applicable to both the machine learning and software engineering communities.