I would fix the evaluation oracles before expanding the model comparison. In the released tests/Stegano/robustness_test.py, all four tests use assert True, including import-failure branches; _run_case treats any caught Exception as success. The evaluator scores pytest passes, not the diagnostic JSON, so these failures receive robustness credit. Stegano's performance test discards the decoded message and checks only positive timings. Assert documented outcomes, permitted exceptions, and decoded-message equality instead. Audit the remaining suites, identify which revisions produced the reported scores, and rerun affected model and reference implementations with corrected tests. Extend the two-defect sanity check with independently selected mutations and incomplete implementations, reporting surviving faults.

The novelty is incremental. NL2Repo-Bench already evaluates installable Python repositories generated from requirements; Commit0 (arXiv:2412.01769) reconstructs libraries with scaffolds and interactive tests; NoFunEval (COLM 2024, arXiv:2401.15963) evaluates multiple non-functional requirements through editing and classification. RAL-Bench's defensible contribution is the combination of concise requirements, fixed public-interface tests, and repository-level quality measurements. Show what this combination reveals beyond earlier protocols. Correct the repository-exclusion claim: tinydb, pyjwt, and cachetools occur in Commit0, and requests occurs in the already-cited RepoCraft. Distinguish this overlap from possible training contamination; investigate the latter with code-similarity measurements and newly authored behavioral variants.

Although failed runtime suites already score zero, successful execution must also validate their outputs. Compare non-functional quality on implementations completing the same validated workload, alongside unconditional scores and failure counts. Otherwise incomplete code can appear secure or efficient. Report raw measurements and reference self-scores: Eq. 1 assigns reference-level maintainability 50%, unlike the 100% anchors elsewhere, so cross-dimension percentage comparisons are misleading. In measure_generated.py, blocking stdout.readline() precedes resource sampling and timeout rechecks. Decouple output collection and verify sampling and timeout enforcement on quiet, long-running workloads.

Make specification adequacy an experiment. Mapping a test to upstream documentation does not establish that its expectation appears in the model's input. Audit assertion-to-input traceability and compare original and clarified specifications under matched settings. Section IV-A raises GPT-5.2 from 41.81% to 49.72% through clarification: explain which tasks improve and whether model rankings change. Restrict “no model exceeds 45%” to the original single-turn prompts; the clarified and agentic settings exceed it.

Finally, reconcile the release with the paper: run_benchmark.py uses temperature=0.2 and supplies a required-file list, whereas the manuscript describes greedy decoding and removing layout hints. Pin the scripts and prompts behind each table. The released runner does not clear existing generation directories and can load stale results after failed generation; establish whether the experiments prevented this externally, and enforce fresh directories and failure-specific results. Report generation limits and truncations, compare strategies under matched budgets and feedback access, and give confidence intervals for task-paired score differences.
