GenCode: A Generic Data Augmentation Framework for Boosting Deep Learning-Based Code Understanding

Abstract: Pre-trained code models lead the era of code intelligence, with multiple models designed with impressive performance. However, one important problem, data augmentation for code data that automatically helps developers prepare training data lacks study in this field. In this paper, we introduce a generic data augmentation framework, GenCode, to enhance the training of code understanding models. Simply speaking, GenCode follows a generation-and-selection paradigm to prepare useful training code data. Specifically, it employs code augmentation techniques to generate new code candidates first and then identifies important ones as the training data by influence scores. To evaluate the effectiveness of GenCode, we conduct experiments on four code understanding tasks (e.g., code clone detection) and three pre-trained code models (e.g., CodeT5) and two recent released code-specific LLMs (e.g., Qwen2.5-Coder). Compared to the state-of-the-art (SOTA) code augmentation method MixCode, GenCode produces pre-trained code models with 2.92% higher accuracy and 4.90% adversarial robustness on average. For code-specific LLMs, GenCode achieves an average improvement of 0.93% in accuracy and 0.98% in natural robustness.

• The paper presents GenCode, a dual-stage framework that generates augmented code samples using both semantic-preserving and syntax-breaking transformations followed by loss-based selection.
• Experimental results show up to a 4.52% increase in accuracy and an 8.42% reduction in attack success rate across various tasks and models.
• The framework accelerates convergence and provides robust data augmentation, setting new standards for improving deep learning-based code understanding.

GenCode: A Detailed Analysis of a Data Augmentation Framework for Code Understanding

This essay presents an in-depth examination of "GenCode: A Generic Data Augmentation Framework for Boosting Deep Learning-Based Code Understanding" (2402.15769). The paper introduces an innovative approach, GenCode, designed to enhance code understanding models through a structured, generation-and-selection mechanism. This analysis provides a thorough overview of the methodology, results, and implications of the proposed framework for experienced researchers in the field.

Introduction to GenCode Framework

GenCode is proposed to tackle the challenges associated with training data preparation for code models, which is often labor-intensive and costly. Traditional methods such as code refactoring have shown limited benefits in improving model performance. GenCode adopts a dual-stage approach: it first generates potential training data using various code transformation techniques, then selects the most informative samples using importance metrics based primarily on loss values. This methodology aims to improve model accuracy and robustness, creating a more versatile framework applicable across various programming tasks and models.

Methodology

Data Generation and Selection Paradigm

GenCode differentiates itself by integrating both semantic-preserving and syntax-breaking code transformations to augment datasets:

• Semantic-Preserving Methods: These techniques, including traditional code refactoring, alter code structure without changing its functionality.
• Syntax-Breaking Methods: Inspired by advances in natural language processing, these methods intentionally modify code syntax to challenge model generalization.

The selection process involves computing the loss value for each generated sample, ranking them, and choosing the top $K$ samples for model training, where $K$ matches the original training dataset size.

Figure 1: Correlation between loss values and code model accuracy.

Search Space and Importance Metric

GenCode’s effectiveness relies on its expansive search space, currently supporting 18 semantic-preserving and 5 syntax-breaking transformations. Key to its approach is the assumption corroborated by the paper’s preliminary study, that samples with higher loss values more significantly contribute to model refinement.

Figure 2: Workflow of GenCode in one training epoch.

Experimental Results

GenCode was evaluated across multiple tasks (bug detection, authorship attribution, and problem classification) and models (CodeBERT, GraphCodeBERT, and CodeT5). Experimental results highlighted:

• Accuracy Improvement: GenCode enhanced model accuracy by up to 4.52% over models trained without augmentation and surpassed existing methods like MixCode by a significant margin.
• Robustness Gains: The framework reduced the attack success rate by 8.42% on average, underscoring its efficacy in bolstering model robustness against adversarial attacks.
• Efficiency of Convergence: The methodology accelerated convergence across all training phases, as evidenced by consistent superiority during training epochs.

  Figure 3: Convergence speed of CodeBERT using different code augmentation methods in each task.

Discussion and Implications

The implications of GenCode’s results are multifold:

• Theoretical Contributions: By validating that high-loss samples enhance learning, GenCode challenges and expands existing narratives in data augmentation research.
• Practical Applications: The implementation of GenCode can lead to the development of more robust coding assistants and automated bug detection systems, enhancing their resilience and accuracy.
• Future Research: Potential expansions include integrating uncertainty metrics and applying GenCode to fine-tune LLMs, such as Llama, to enhance generalization across diverse programming languages.

Figure 4: Visualization of code embeddings after dimension reduction using Principal Component Analysis (PCA). Model: CodeBERT, dataset: Refactory, task: Bug detection.

Conclusion

GenCode represents a significant advancement in data augmentation for code understanding. It combines innovative methodologies to improve both the accuracy and robustness of deep learning models. GenCode sets a new standard in the field, demonstrating how structured data generation and selection can effectively refine neural models. Future work may build upon these findings to explore more complex code transformations and the integration of additional importance metrics.