title: EpiCoder: Encompassing Diversity and Complexity in Code Generation
summary: Effective instruction tuning is indispensable for optimizing code LLMs,
  aligning model behavior with user expectations and enhancing model performance
  in real-world applications. However, most existing methods focus on code
  snippets, which are limited to specific functionalities and rigid structures,
  restricting the complexity and diversity of the synthesized data. To address
  these limitations, we introduce a novel feature tree-based synthesis framework
  inspired by Abstract Syntax Trees (AST). Unlike AST, which captures syntactic
  structure of code, our framework models semantic relationships between code
  elements, enabling the generation of more nuanced and diverse data. The feature
  tree is constructed from raw data and refined iteratively to increase the
  quantity and diversity of the extracted features. This process enables the
  identification of more complex patterns and relationships within the code. By
  sampling subtrees with controlled depth and breadth, our framework allows
  precise adjustments to the complexity of the generated code, supporting a wide
  range of tasks from simple function-level operations to intricate multi-file
  scenarios. We fine-tuned widely-used base models to create the EpiCoder series,
  achieving state-of-the-art performance at both the function and file levels
  across multiple benchmarks. Notably, empirical evidence indicates that our
  approach shows significant potential in synthesizing highly complex
  repository-level code data. Further analysis elucidates the merits of this
  approach by rigorously assessing data complexity and diversity through software
  engineering principles and LLM-as-a-judge method.
paper_authors._id: 677f68d5b8f3572fc35d6046
paper_authors.name: Yaoxiang Wang
paper_authors._id: 677f68d5b8f3572fc35d6047
paper_authors.name: Haoling Li
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-01-09T10:06:15.412Z
paper_authors._id: 650be23ec4e52db6a4db63ef
paper_authors.avatarUrl: /avatars/03af548029b38bee49ec295fefe74f9a.svg
paper_authors.fullname: Haoling Li
paper_authors.type: user
paper_authors.user: Ringo1110
paper_authors._id: 677f68d5b8f3572fc35d6048
paper_authors.name: Xin Zhang
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2025-01-09T06:12:38.212Z
paper_authors._id: 641a9a4b05290a135041a3ed
paper_authors.avatarUrl: /avatars/95d66ac607973abe95bd3558c6c93739.svg
paper_authors.fullname: Pluto
paper_authors.type: user
paper_authors.user: CharonBony
paper_authors._id: 677f68d5b8f3572fc35d6049
paper_authors.name: Jie Wu
paper_authors._id: 677f68d5b8f3572fc35d604a
paper_authors.name: Xiao Liu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-01-09T19:57:52.589Z
paper_authors._id: 63fb6e281b4b1bd4e7ffc5be
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1677422062937-noauth.jpeg
paper_authors.fullname: Xiao Liu
paper_authors.type: user
paper_authors.user: lx865712528
paper_authors._id: 677f68d5b8f3572fc35d604b
paper_authors.name: Wenxiang Hu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-09T20:31:18.292Z
paper_authors._id: 6327cafc46ffcfc27cfa4c9a
paper_authors.avatarUrl: /avatars/4daea27b432ff428c41d590669f7330c.svg
paper_authors.fullname: Wenxiang Hu
paper_authors.type: user
paper_authors.user: wenxcs
paper_authors._id: 677f68d5b8f3572fc35d604c
paper_authors.name: Zhongxin Guo
paper_authors._id: 677f68d5b8f3572fc35d604d
paper_authors.name: Yangyu Huang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-09T20:31:33.079Z
paper_authors._id: 64c66647725ffa04b2fd6c94
paper_authors.avatarUrl: /avatars/620f63f27fa1e90423b0dc22aa8e5809.svg
paper_authors.fullname: yangyu huang
paper_authors.type: user
paper_authors.user: yangyu90
paper_authors._id: 677f68d5b8f3572fc35d604e
paper_authors.name: Ying Xin
paper_authors._id: 677f68d5b8f3572fc35d604f
paper_authors.name: Yujiu Yang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-09T20:31:49.764Z
paper_authors._id: 64ca1fe838837b12d5e529b7
paper_authors.avatarUrl: /avatars/44a3ad9e59318784ac531993b5f69f6b.svg
paper_authors.fullname: Yujiu Yang
paper_authors.type: user
paper_authors.user: Thu-redrobot
paper_authors._id: 677f68d5b8f3572fc35d6050
paper_authors.name: Jinsong Su
paper_authors._id: 677f68d5b8f3572fc35d6051
paper_authors.name: Qi Chen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-09T20:32:03.897Z
paper_authors._id: 6358c7db856b319a29bdb26b
paper_authors.avatarUrl: /avatars/c806e76b6342c1b788bd1dc337f086b8.svg
paper_authors.fullname: QiChen
paper_authors.type: user
paper_authors.user: QiChen00
paper_authors._id: 677f68d5b8f3572fc35d6052
paper_authors.name: Scarlett Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-09T20:32:10.147Z
paper_authors._id: 67366efb049bfa3a9084e8d1
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/Bny5bUc6qFQ_RWpEKpRAW.jpeg
paper_authors.fullname: Scarlett Li
paper_authors.type: user
paper_authors.user: lisijia0504
paper_title: EpiCoder: Encompassing Diversity and Complexity in Code Generation
paper_summary: Effective instruction tuning is indispensable for optimizing code LLMs,
  aligning model behavior with user expectations and enhancing model performance
  in real-world applications. However, most existing methods focus on code
  snippets, which are limited to specific functionalities and rigid structures,
  restricting the complexity and diversity of the synthesized data. To address
  these limitations, we introduce a novel feature tree-based synthesis framework
  inspired by Abstract Syntax Trees (AST). Unlike AST, which captures syntactic
  structure of code, our framework models semantic relationships between code
  elements, enabling the generation of more nuanced and diverse data. The feature
  tree is constructed from raw data and refined iteratively to increase the
  quantity and diversity of the extracted features. This process enables the
  identification of more complex patterns and relationships within the code. By
  sampling subtrees with controlled depth and breadth, our framework allows
  precise adjustments to the complexity of the generated code, supporting a wide
  range of tasks from simple function-level operations to intricate multi-file
  scenarios. We fine-tuned widely-used base models to create the EpiCoder series,
  achieving state-of-the-art performance at both the function and file levels
  across multiple benchmarks. Notably, empirical evidence indicates that our
  approach shows significant potential in synthesizing highly complex
  repository-level code data. Further analysis elucidates the merits of this
  approach by rigorously assessing data complexity and diversity through software
  engineering principles and LLM-as-a-judge method.
paper_ai_keywords: Abstract Syntax Trees (AST) feature tree-based synthesis semantic relationships feature tree subtrees EpiCoder base models state-of-the-art performance software engineering principles LLM-as-a-judge