[2604.06742] Evaluating LLM-Based 0-to-1 Software Generation in End-to-End CLI Tool Scenarios

Computer Science > Software Engineering arXiv:2604.06742 (cs) [Submitted on 8 Apr 2026] Title:Evaluating LLM-Based 0-to-1 Software Generation in End-to-End CLI Tool Scenarios Authors:Ruida Hu, Xinchen Wang, Chao Peng, Cuiyun Gao, David Lo View a PDF of the paper titled Evaluating LLM-Based 0-to-1 Software Generation in End-to-End CLI Tool Scenarios, by Ruida Hu and 4 other authors View PDF HTML (experimental) Abstract:Large Language Models (LLMs) are driving a shift towards intent-driven development, where agents build complete software from scratch. However, existing benchmarks fail to assess this 0-to-1 generation capability due to two limitations: reliance on predefined scaffolds that ignore repository structure planning, and rigid white-box unit testing that lacks end-to-end behavioral validation. To bridge this gap, we introduce CLI-Tool-Bench, a structure-agnostic benchmark for evaluating the ground-up generation of Command-Line Interface (CLI) tools. It features 100 diverse real-world repositories evaluated via a black-box differential testing framework. Agent-generated software is executed in sandboxes, comparing system side effects and terminal outputs against human-written oracles using multi-tiered equivalence metrics. Evaluating seven state-of-the-art LLMs, we reveal that top models achieve under 43% success, highlighting the ongoing challenge of 0-to-1 generation. Furthermore, higher token consumption does not guarantee better performance, and agents tend to g...