arXiv AI By Songwei Yu, Ziqi Miao, Jingxuan Xu, Junhao Fan, Haoyang Huang, Yuan Xiong, Zihang Xu, Weihao Li, Wenqiang Zhu, Yuechen Luo, Xinshun Feng, Yanan Wu, Ken Deng

SWE-Nexus: Automated Composition of Software Engineering Tasks from Test Execution

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SWE‑Nexus is a fully automated pipeline that builds repository‑level implementation tasks by analyzing test execution. It uses four construction agents to select tests, trace production function bodies, and generate behavioral specifications, retaining scopes only when all expected scoring points pass and masking the traced bodies triggers failures. The benchmark covers 600 tasks from 105 repositories in six languages, and the best configuration resolves 37.8% of tasks in a single attempt, demonstrating the system’s ability to construct tasks from validated behavior beyond individual code changes.

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arXiv AI
Oct 1

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