EpiCoder: A New Approach to Generating Complex and Diverse Code

Effectively fine-tuning large language models (LLMs) for code generation is crucial to aligning model behavior with user expectations and improving performance in real-world applications. Previous methods often focused on code snippets that are limited in their functionality and structure, thus restricting the complexity and diversity of the synthesized data. A new approach called EpiCoder utilizes an innovative, feature-tree-based synthesis framework inspired by Abstract Syntax Trees (ASTs).

Unlike ASTs, which capture the syntactic structure of code, the feature-tree framework models the semantic relationships between code elements. This enables the generation of more differentiated and diverse data. The feature tree is constructed from raw data and iteratively refined to increase the quantity and diversity of the extracted features. This process allows for the identification of more complex patterns and relationships within the code.

Through the controlled selection of subtrees with specific depth and breadth, the framework allows precise adjustment of the complexity of the generated code. It thus supports a wide range of tasks, from simple operations at the function level to complex scenarios involving multiple files. Fine-tuning common base models led to the development of the EpiCoder series, which achieves state-of-the-art performance at both the function and file levels in several benchmarks.

How EpiCoder Works

The EpiCoder approach is based on a three-stage process:

1. Feature Tree Extraction: First, a feature set is extracted from the raw data to create a tree structure. This serves as a template for the LLM to directly extract tree structures from the code data. 2. Feature Tree Evolution: The feature tree is iteratively expanded, both in breadth and depth, to increase the diversity of features. This enables the capture of more complex code structures and patterns. 3. Feature Tree-Based Code Generation: Subtrees are selected from the evolved feature tree to generate diverse code and instruction data. The complexity of the generated code can be controlled by the selection of subtrees.

Evaluation and Results

EpiCoder was evaluated using various benchmarks and showed outstanding results in code generation. The models trained with EpiCoder surpassed comparable models in terms of performance, both at the function and file levels. The potential of EpiCoder for synthesizing complex data at the repository level is particularly promising.

Analysis of the generated data using software engineering principles and LLM-based evaluation methods confirmed the high complexity and diversity of the code. These results underscore the advantages of the feature-tree-based approach for code generation.

Significance for the Future of Code Generation

EpiCoder represents a significant advancement in the development of LLMs for code generation. By modeling semantic relationships and enabling controlled complexity management, EpiCoder allows for the generation of more realistic and diverse code. This approach could drive the development of more powerful AI-assisted code generation tools and significantly influence software development in the future.

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