"EpiCoder: Encompassing Diversity and Complexity in Code Generation"

EpiCoder's tree-based approach lets it scale from writing functions to entire code repositories.

EpiCoder introduces a feature tree framework for code generation that captures semantic relationships between code elements, enabling more complex and diverse code synthesis than traditional snippet-based methods.

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https://arxiv.org/abs/2501.04694

Original Problem 🤔:

Current code instruction tuning methods rely on rigid code snippets, limiting the complexity and diversity of synthesized data for training LLMs. This restricts their ability to handle real-world programming scenarios.

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Solution in this Paper 🔧:

→ The framework builds feature trees from code by extracting semantic relationships between elements, unlike Abstract Syntax Trees that only capture syntax

→ Features are organized hierarchically and evolved through depth and breadth expansion to increase diversity

→ Subtrees are sampled with controlled complexity to generate code ranging from functions to multi-file projects

→ The framework adjusts feature sampling probabilities to prioritize underrepresented knowledge areas

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Key Insights 💡:

→ Semantic relationships between code elements are more valuable than pure syntax for generating diverse code

→ Hierarchical feature organization enables controlled scaling of code complexity

→ Feature evolution is more efficient than evolving individual code snippets

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Results 📊:

→ EpiCoder-Qwen-7B achieves SOTA performance on 5 function-level benchmarks

→ Successfully generates complex repository-level code with 50+ files

→ Shows 2.55x improvement in unique operators and 20.99x in unique operands vs baselines

→ Demonstrates superior performance on file-level XFileDep benchmark

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