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

2026-07-20 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Expert, extended

Summary

CLI-Tool-Bench introduces a novel benchmark designed to evaluate LLM-based 0-to-1 software generation, specifically for Command-Line Interface (CLI) tools. This benchmark addresses limitations of existing evaluations that rely on predefined structures and white-box testing. CLI-Tool-Bench utilizes a structure-agnostic, black-box differential testing framework comprising 100 high-quality, real-world repositories across Python, JavaScript, and Go. It features an automated pipeline for schema extraction and test case generation, evaluating agents in isolated Docker sandboxes using multi-tiered equivalence metrics (Execution, Exact, Fuzzy, Semantic Match) and system-level side-effect consistency. Experiments with seven LLMs and two agent frameworks (OpenHands, Mini-SWE-Agent) reveal top models achieve less than 43% overall success, highlighting significant challenges. The study also notes a non-monotonic performance trend with complexity, that higher token consumption does not equate to better results, and agents prefer monolithic code structures. Surprisingly, LLM-generated tools often exhibit faster runtime due to their minimalist design.

Key takeaway

For AI Engineers developing agentic software, recognize that current LLM agents struggle with end-to-end software generation, achieving less than 43% success. You should provide explicit structural scaffolding or mandate standardized CLI frameworks to guide their monolithic design preference. Always treat agent-generated code as functional prototypes, actively reviewing and reinforcing it with robust error handling and security measures before production deployment, despite its potential for faster raw execution.

Key insights

LLM agents struggle with end-to-end software generation, often preferring monolithic designs over modularity.

Principles

• LLM agents prefer monolithic designs for context management.
• Higher token consumption does not equate to better task resolution.
• Traditional string-based evaluations are insufficient for agentic tasks.

Method

The CLI-Tool-Bench pipeline evaluates 0-to-1 CLI tool generation via repository curation, LLM-directed schema extraction and fuzzing for test synthesis, and black-box differential testing in Docker sandboxes using multi-tiered equivalence metrics.

In practice

• Provide explicit structural scaffolding for LLM agents.
• Mandate standardized CLI frameworks like Click or Cobra.
• Review agent-generated code for error handling and security.

Topics

• LLM Agents
• Software Generation Benchmarks
• CLI Tools
• Black-Box Testing
• Code Evaluation Metrics
• Agentic Workflows

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.