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TreeGen: A Tree-Based Transformer Architecture for Code Generation (1911.09983v2)

Published 22 Nov 2019 in cs.LG and cs.SE

Abstract: A code generation system generates programming language code based on an input natural language description. State-of-the-art approaches rely on neural networks for code generation. However, these code generators suffer from two problems. One is the long dependency problem, where a code element often depends on another far-away code element. A variable reference, for example, depends on its definition, which may appear quite a few lines before. The other problem is structure modeling, as programs contain rich structural information. In this paper, we propose a novel tree-based neural architecture, TreeGen, for code generation. TreeGen uses the attention mechanism of Transformers to alleviate the long-dependency problem, and introduces a novel AST reader (encoder) to incorporate grammar rules and AST structures into the network. We evaluated TreeGen on a Python benchmark, HearthStone, and two semantic parsing benchmarks, ATIS and GEO. TreeGen outperformed the previous state-of-the-art approach by 4.5 percentage points on HearthStone, and achieved the best accuracy among neural network-based approaches on ATIS (89.1%) and GEO (89.6%). We also conducted an ablation test to better understand each component of our model.

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Authors (6)
  1. Zeyu Sun (33 papers)
  2. Qihao Zhu (27 papers)
  3. Yingfei Xiong (33 papers)
  4. Yican Sun (10 papers)
  5. Lili Mou (79 papers)
  6. Lu Zhang (373 papers)
Citations (161)
View on Semantic Scholar

Summary

TreeGen: A Tree-Based Transformer Architecture for Code Generation

This paper introduces TreeGen, a novel architecture designed to improve code generation using neural networks by addressing the challenges inherent in modeling the structural dependencies of programming languages. Code generation systems are tasked with converting natural language descriptions into executable code, and existing approaches typically rely on neural networks, such as sequence-to-sequence (Seq2Seq) models, which often face difficulties due to long dependency issues and insufficient representation of code structures.

Key Contributions

TreeGen leverages a tree-based neural architecture with the following innovative components:

  1. Transformer Architecture: TreeGen employs a Transformer model, known for its ability to capture long-range dependencies through attention mechanisms, which is crucial for resolving the long dependency problem in code generation.
  2. AST Reader: A crucial innovation of TreeGen is its integration of an Abstract Syntax Tree (AST) reader. The AST reader encodes structural information of the code using grammar rules and AST structures, providing a richer representation during the generation process.
  3. Structural Convolution Layers: TreeGen introduces structural convolution layers only in the initial blocks of the AST reader's decoder, rather than throughout the entire network. This design choice maintains information fidelity and effectively blends each node's vector representation with its structural context.
  4. Evaluation and Results: The architecture was tested on a Python benchmark, HearthStone, and two semantic parsing datasets, ATIS and GEO. Notably, TreeGen outperformed previous state-of-the-art approaches by a significant margin of 4.5 percentage points on the HearthStone benchmark, achieving the highest accuracy among neural network-based methods at 89.1% for ATIS and 89.6% for GEO.

Detailed Evaluation and Analysis

The paper provides a comprehensive evaluation framework, including both qualitative and quantitative analyses. In the ablation tests, components like tree convolution, rule definition encoding, and character embeddings were systematically removed to assess their impact, highlighting the importance of each component in overall performance.

The authors performed time efficiency analyses showing superior computational performance of TreeGen compared to past approaches, with faster training times and better utilization of hardware resources. Furthermore, experiments were conducted to determine the optimal location for adding structural convolution sub-layers within the architecture.

Implications and Future Work

TreeGen's advancements underscore the importance of structural modeling in code generation tasks. The paper suggests that future innovations might include expanding these models to a wider range of programming languages and complex coding scenarios. Additionally, further exploration could involve improving the integration of structural network layers based on the semantics of programming tasks, potentially leading to even more robust and efficient code generation systems.

The implications for AI development are profound as TreeGen paves the way for more intelligent systems to assist developers, reducing the cognitive load and improving productivity through automated code synthesis. Future research could explore enhancing these models with adaptive learning techniques or integrating them within collaborative AI-assisted programming environments.

Overall, TreeGen represents a significant step forward in leveraging deep learning architectures to address complex challenges in code generation, setting a foundation for further exploration and refinement in AI-driven software development tools.