arXiv AI

ProjAgent: Procedural Similarity Retrieval for Repository-Level Code Generation

arXiv:2607. 08691v1 Announce Type: cross Abstract: Repository-level code generation requires implementing target functions while accounting for complex cross-file dependencies and project-specific conventions.

arXiv AI
Aug 11

A Unified Issue Resolution Benchmark for Requirement Clarification, Planning, and Code Generation for Coding Agents

arXiv:2608. 09072v1 Announce Type: cross Abstract: Large language model-powered coding agents are increasingly used to modify existing code repositories, for example, by adding features or fixing bugs.

By Xin Zhou, Chun Yong Chong, Kisub Kim, Yun Peng, Rui Shu, Zihan Wu, Xu Han, Guowen Yuan, Zeyang Zhuang, Jounghoon Kim, Jeongjin Ju, Seongmin Ju, Taein Yoon, David Lo
arXiv AI
Jun 16

AlignCoder: Aligning Retrieval with Target Intent for Repository-Level Code Completion

arXiv:2601. 19697v2 Announce Type: replace-cross Abstract: Repository-level code completion remains a challenging task for existing code large language models (code LLMs) due to their limited understanding of repository-specific context and domain knowledge.

By Tianyue Jiang, Yanli Wang, Yanlin Wang, Daya Guo, Ensheng Shi, Yuchi Ma, Jiachi Chen, Zibin Zheng
arXiv AI
3d ago

E2E-SWE: Benchmarking LLMs on Building Working Codebases from Scratch

E2E-SWE is a benchmark that tests large language models’ ability to create complete, functional software repositories from scratch. It includes 186 tasks across 11 programming languages, each requiring an agent to build an installable project based solely on a natural‑language specification and an empty workspace, while passing a hidden test suite. The benchmark was crafted by software engineers and LLMs, then refined through iterative verification by autonomous agents to ensure clarity and solvability.

By Hantian Ding, Chloe Bi, Jiacheng Zhu, John Yang, Matt Deitke, Pengcheng Yin, Zijian Wang, Rui Hou
arXiv AI
Sep 3

PaperCompiler: Faithful Paper-to-Code Generation via Repository-Level Specification Compilation

PaperCompiler is a framework that translates research papers into repository-level code by compiling paper-grounded evidence into explicit implementation specifications. It preserves source provenance, distinguishes between paper-supported, inferred, externally delegated, and unresolved information, and encodes requirements such as non-degradation, ownership, cross-file dependencies, and file-level constraints. The framework improves reference-based fidelity by 13.8% and cuts high-severity evaluator critiques from 13.2% to 6.1% on Paper2CodeBench.

By Yunhao Liu, Hong Phuc Pham, Jaehong Yoon
Hugging Face Trending Papers
Sep 2

PaperCompiler: Faithful Paper-to-Code Generation via Repository-Level Specification Compilation

PaperCompiler is a framework that translates research papers into repository-level code by compiling paper-grounded evidence into explicit implementation specifications. It preserves source provenance, distinguishes between paper-supported, inferred, externally delegated, and unresolved information, and encodes non-degradation requirements, ownership, cross-file dependencies, and file-level constraints. The approach outperforms baselines on Paper2CodeBench, improving reference-based fidelity by 13.8% and halving high-severity evaluator critiques.

arXiv AI
Sep 2

WiseSpec: Requirements-Driven Agents for Code Generation

WiseSpec is a requirements‑driven agent framework designed to improve repository‑level code generation. It automatically builds structured, information‑rich requirements, evaluates their quality via execution‑based tests, and iteratively refines them to better guide code generation. Experiments show WiseSpec outperforms all baselines, achieving an average 13.17% improvement in %Resolved.

By Zhao Tian
arXiv AI
6d ago

Compact Documentation for Coding Agents: A Benchmark, an Optimizer, and Why It Does Not Transfer

The paper explores whether natural‑language documentation aids coding agents in fixing software bugs and introduces a roundtrip benchmark that evaluates code descriptions by regenerating code and testing it. It finds that description completeness, not length, determines fidelity, and presents an optimizer that can produce fully faithful descriptions that generalize to new files. However, experiments across two model families and ten repositories show that such compact documentation does not improve an agent’s ability to resolve real repository issues compared to using the issue alone.

By Md Shohel Arman, Igor Molybog
arXiv AI
Sep 24

Schr\"odinger's Code Repository: Have LLMs Learned SWE-bench or Memorized It?

Schr"odinger's Repository (Schr"odingerRepo) is an evaluation framework that tests coding agents on dynamically instantiated repository representations to mitigate data leakage from static repository benchmarks. It transforms test repositories through four levels—problem statement reconstruction, namespace remapping, intra-file layout reordering, and functionality-preserving code rewriting—to obscure familiar cues while preserving executable behavior. Experiments on popular LLMs using SWE-bench Verified and SWE-QA show that removing these cues consistently degrades performance and increases interaction costs, mainly due to harder repository exploration and localization.

By Silin Chen, Yufei Yang, Xiaodong Gu, Yuling Shi, Chengcheng Wan, Haibing Guan
arXiv Machine Learning
Aug 6

ReCodeAgent: A Multi-agent Workflow for Language-Agnostic Translation and Validation of Large-Scale Repositories

arXiv:2604. 07341v2 Announce Type: replace-cross Abstract: Most repository-level code translation and validation techniques have been evaluated on a single source-target programming language (PL) pair, owing to the complex engineering effort required to adapt new PL pairs.

By Ali Reza Ibrahimzada, Brandon Paulsen, Daniel Kroening, Reyhaneh Jabbarvand