Code Generation as a Dual Task of Code Summarization (1910.05923v1)

Abstract: Code summarization (CS) and code generation (CG) are two crucial tasks in the field of automatic software development. Various neural network-based approaches are proposed to solve these two tasks separately. However, there exists a specific intuitive correlation between CS and CG, which have not been exploited in previous work. In this paper, we apply the relations between two tasks to improve the performance of both tasks. In other words, exploiting the duality between the two tasks, we propose a dual training framework to train the two tasks simultaneously. In this framework, we consider the dualities on probability and attention weights, and design corresponding regularization terms to constrain the duality. We evaluate our approach on two datasets collected from GitHub, and experimental results show that our dual framework can improve the performance of CS and CG tasks over baselines.

• The paper introduces a novel dual training framework that simultaneously trains code summarization and generation models by exploiting their inherent duality.
• Experimental results on Java and Python datasets show the dual framework significantly improves performance metrics (BLEU, METEOR, ROUGE-L) for both tasks compared to independent training.
• This work demonstrates the potential of dual task learning for automated software development and suggests future research integrating grammar rules or expanding to other languages.

Dual Task Framework for Code Summarization and Code Generation

This paper presents a novel approach aimed at enhancing the performance of two critical tasks in automatic software development: Code Summarization (CS) and Code Generation (CG). These tasks traditionally rely on neural network-based methodologies that have been developed independently. The authors propose a dual training framework that simultaneously trains models for both tasks by exploiting their inherent duality. They hypothesize and demonstrate that by considering the duality of CS and CG (where the input of one is the output of the other, and vice versa), improvements can be achieved across both tasks.

Methodology

The proposed framework employs an encoder-decoder architecture with an attention mechanism, similar to standard machine translation models. The authors introduce two novel constraints into the dual training process:

1. Probabilistic Duality Constraint: This constraint leverages the probabilistic relationship between CS and CG modeled through joint probability. By incorporating a regularization term based on the inverse conditional probabilities, the training process encourages the models to maintain consistency in this probabilistic duality.
2. Attention Duality Constraint: Recognizing that the attention mechanisms between CS and CG models should reflect similar token alignments, the authors propose a regularization term based on the Jensen-Shannon divergence. This term ensures that attention weights between tokens in both tasks remain symmetric, potentially leading to better semantic alignment during the training process.

The models were evaluated on datasets sourced from Java and Python projects on GitHub, indicating that the joint training framework yields superior performance over baseline models that train CS and CG independently.

Experimental Results

The results demonstrate the efficacy of the dual framework, notably enhancing both BLEU, METEOR, and ROUGE-L scores for CS tasks, and BLEU scores for CG tasks. The results indicate that the joint training approach significantly improves over independently trained models as well as established baselines like CODE-NN and DeepCom.

Moreover, the framework shows an increase in the percentage of syntactically valid code produced, highlighting the practicality of such dual-task approaches in generating usable code snippets.

Contributions and Implications

The authors make several key contributions including:

• The initial proposition and implementation of a dual learning framework that concurrently trains models for CS and CG.
• The incorporation of novel constraints based on duality principles, specifically leveraging attention symmetries and probabilistic correlations.

This work has significant implications for the field of AI-driven software development. By demonstrating that the relationship between CS and CG can be jointly harnessed, it provides a new methodological framework that could inspire further research into multi-task and transfer learning within software engineering. The approach could potentially be extended to other related tasks or integrated with additional knowledge sources, such as grammar rules, to enhance performance further.

Future Directions

The paper outlines potential future directions: leveraging additional sources of information, such as grammar rules, within this dual framework could further boost performance outcomes. Additionally, expanding this approach to other programming languages or code generation frameworks may provide broader applicability and insights into the generalizability of dual task learning methodologies.

In summary, this work provides a significant step forward in the automated software development landscape by recognizing and capitalizing on the inherent duality between code summarization and generation tasks, setting a foundation for future advancements in this domain.