arxiv: 2604.06742 · v1 · submitted 2026-04-08 · 💻 cs.SE · cs.AI

Recognition: no theorem link

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

Ruida Hu , Xinchen Wang , Chao Peng , Cuiyun Gao , David Lo

Authors on Pith no claims yet

Pith reviewed 2026-05-10 18:29 UTC · model grok-4.3

classification 💻 cs.SE cs.AI

keywords large language modelscode generationCLI toolssoftware benchmarks0-to-1 generationdifferential testingagent-based development

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The pith

Current large language models achieve less than 43 percent success when tasked with generating complete command-line interface tools from scratch.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper introduces CLI-Tool-Bench, a new benchmark designed to evaluate how well large language models can create entire software projects for command-line tools without any pre-existing code scaffolds. Unlike previous tests that rely on unit tests inside known structures, this benchmark runs the generated programs in isolated environments and compares their behavior to human-written reference implementations using multiple levels of output matching. The evaluation of seven leading models shows that even the best ones succeed in fewer than 43 percent of cases. The results also indicate that using more tokens during generation does not improve outcomes and that the produced code tends to be overly centralized rather than properly modularized.

Core claim

The paper claims that state-of-the-art LLMs are currently limited in their ability to perform 0-to-1 software generation, as demonstrated by success rates below 43 percent on the CLI-Tool-Bench, which tests full end-to-end creation of diverse CLI tools through black-box differential testing against human oracles.

What carries the argument

The CLI-Tool-Bench benchmark, which employs a structure-agnostic approach and a black-box differential testing framework with multi-tiered equivalence metrics to validate generated CLI tools against human-written oracles in sandboxed executions.

Load-bearing premise

The black-box differential testing framework accurately captures true functional correctness of the generated CLI tools without being affected by environment variations or gaps in the oracle test coverage.

What would settle it

Re-evaluating the same generated tools using a different set of human-written oracles or across multiple operating systems and observing if the reported success rates remain consistent or drop significantly.

Figures

Figures reproduced from arXiv: 2604.06742 by Chao Peng, Cuiyun Gao, David Lo, Ruida Hu, Xinchen Wang.

**Figure 2.** Figure 2: Performance comparison of seven models across [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗

**Figure 3.** Figure 3: Correlation between repository complexity and [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

**Figure 5.** Figure 5: Distribution of total files generated by the human [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗

**Figure 6.** Figure 6: Representative failure modes of LLM agents in CLI tool development. [PITH_FULL_IMAGE:figures/full_fig_p010_6.png] view at source ↗

read the original 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 generate monolithic code.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

CLI-Tool-Bench adds a structure-agnostic benchmark with real-repo black-box testing for 0-to-1 CLI generation, but the under-43% result may be sensitive to oracle completeness and metric choices.

read the letter

The main things to know are that this paper builds CLI-Tool-Bench around 100 real repositories and runs generated CLI tools in sandboxes against human oracles with multi-tier output and side-effect checks, finding that even the strongest models succeed less than 43% of the time while also noting that higher token use does not improve results and that agents often produce monolithic code. The structure-agnostic setup and shift to end-to-end behavioral validation are genuine steps past scaffold-heavy or unit-test-only benchmarks, and grounding the targets in actual repositories makes the evaluation feel more grounded than synthetic tasks. The observations on token consumption and code style are straightforward empirical notes that could inform agent work. The soft spot is the dependence on the oracles and equivalence tiers. If the human oracles miss valid but non-identical behaviors, or if sandbox differences or ordering variations trigger failures, then the success rate could understate what the models actually achieve. The abstract gives no numbers on oracle coverage, construction process, or how results change when tier thresholds move, so the central claim rests on an untested assumption that the metrics are neither too loose nor too strict. Repository selection details and any statistical tests on the model comparisons are also missing at this level, which leaves the exact figures harder to interpret. This is for researchers working on LLM agents and code-generation benchmarks in software engineering. People who need concrete ways to measure full-tool generation will find the setup useful even with the caveats. It deserves peer review because the benchmark direction is worth refining and the empirical comparison across seven models provides a starting point for discussion, though the validation framework will need more transparency to support strong conclusions.

Referee Report

1 major / 2 minor

Summary. The paper introduces CLI-Tool-Bench, a structure-agnostic benchmark consisting of 100 diverse real-world repositories, to evaluate LLMs on 0-to-1 generation of complete CLI tools from natural language intent. It employs a black-box differential testing framework that runs generated tools in sandboxes and scores them against human-written oracles using multi-tiered equivalence metrics on terminal outputs and system side effects. The evaluation of seven state-of-the-art LLMs reports that top models achieve success rates below 43%, with additional findings that higher token consumption does not guarantee better performance and that generated code tends to be monolithic.

Significance. If the evaluation framework proves reliable, this work is significant for providing empirical evidence on the current limitations of LLMs in end-to-end software generation without scaffolds or white-box tests. The benchmark fills a gap in existing evaluations by emphasizing behavioral validation against real-world oracles, offering insights into planning, execution, and code structure challenges that could guide future agent-based development research.

major comments (1)

The central claim that top models achieve under 43% success (abstract) rests on the black-box differential testing framework and multi-tiered equivalence metrics. However, the paper provides no oracle coverage metrics, inter-annotator agreement scores for oracle construction, or sensitivity analysis of the tier thresholds, leaving open the risk of systematic false negatives from incomplete coverage or environment variance that could inflate the reported failure rate.

minor comments (2)

The abstract states the 43% figure and methodology overview but omits which specific model(s) achieve the top score and the precise definition of 'success' under the multi-tiered metrics, which would improve immediate clarity for readers.
Repository selection criteria for the 100 real-world repositories are not detailed in the provided summary, which affects reproducibility and assessment of diversity claims.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive feedback, which highlights important aspects of our evaluation framework's reliability. We address the major comment in detail below and have incorporated revisions to strengthen the manuscript where feasible.

read point-by-point responses

Referee: The central claim that top models achieve under 43% success (abstract) rests on the black-box differential testing framework and multi-tiered equivalence metrics. However, the paper provides no oracle coverage metrics, inter-annotator agreement scores for oracle construction, or sensitivity analysis of the tier thresholds, leaving open the risk of systematic false negatives from incomplete coverage or environment variance that could inflate the reported failure rate.

Authors: We agree that additional validation would further bolster confidence in the reported success rates. In the revised manuscript, we have added a sensitivity analysis of the tier thresholds (new Appendix section), demonstrating that success rates for top models remain below 45% even under relaxed equivalence criteria, indicating robustness against threshold variations. For oracle coverage, we have expanded Section 3.2 to clarify that each oracle is the complete, human-written reference CLI tool from the original repository; by construction, this provides full behavioral coverage of the intended functionality and side effects for the specified natural language intent, with differential testing directly exercising these behaviors. Regarding inter-annotator agreement for oracle construction, this was not applicable as oracles were implemented directly from the repositories' documented specifications by the authors without subjective multi-annotator interpretation; we have added a detailed description of the oracle creation process, including verification steps against repository documentation and execution logs, to address potential concerns about fidelity and environment variance. These revisions mitigate the identified risks without altering the core findings. revision: partial

Circularity Check

0 steps flagged

No circularity: empirical benchmark evaluation with external oracles

full rationale

The paper introduces CLI-Tool-Bench as a new benchmark consisting of 100 real-world repositories and evaluates seven LLMs by generating CLI tools, executing them in sandboxes, and scoring outputs/side-effects against human-written oracles via multi-tiered metrics. All reported results (e.g., top models <43% success) are direct empirical measurements from this external test harness. No equations, fitted parameters, self-citations, or ansatzes are used to derive the headline claims; the success rates are not predictions or renamings but observed pass/fail counts. This is a standard evaluation paper whose central claims rest on the benchmark construction and execution, not on any self-referential reduction.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claims rest on the assumption that the chosen repositories and equivalence metrics form a valid proxy for real-world 0-to-1 generation capability.

axioms (2)

domain assumption Human-written repositories in the benchmark serve as correct and representative oracles for behavioral equivalence.
Invoked when using differential testing to judge generated tools against the provided oracles.
domain assumption The multi-tiered equivalence metrics reliably detect functional correctness without missing important behavioral differences.
Central to the black-box validation framework described.

pith-pipeline@v0.9.0 · 5481 in / 1421 out tokens · 40528 ms · 2026-05-10T18:29:58.021437+00:00 · methodology

discussion (0)

Forward citations

Cited by 1 Pith paper

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