A Multi-Language Perspective on the Robustness of LLM Code Generation

Fazle Rabbi; Jinqiu Yang; Zishuo Ding

arxiv: 2504.19108 · v6 · submitted 2025-04-27 · 💻 cs.SE

A Multi-Language Perspective on the Robustness of LLM Code Generation

Fazle Rabbi , Zishuo Ding , Jinqiu Yang This is my paper

Pith reviewed 2026-05-22 19:17 UTC · model grok-4.3

classification 💻 cs.SE

keywords LLM code generationrobustnessperturbationsmulti-languagedocstring repairPythonJavaC++

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The pith

LLM code generation models lose performance when prompts receive small changes, with effects varying by language but appearing across Python, Java, and C++.

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

The paper tests several prominent code generation models by adding four kinds of prompt changes: altered docstrings, function names, syntax, and format. It measures how much the generated code quality drops for each language and finds that every model suffers, though the size of the drop depends on the language and the type of change. Semantic alterations turn out to be at least as harmful as syntactic ones, and simply making the model bigger does not produce more stable results. The authors also try using an LLM to fix perturbed docstrings and report only small improvements that sometimes make things worse instead. These findings matter because code generation tools are used with imperfect or varied prompts in practice.

Core claim

All models consistently degrade under perturbations across all three languages, but vary in magnitude depending on the language and perturbation type. Larger model size does not reliably improve robustness, and semantic perturbations prove at least as disruptive as syntactic ones. LLM-based docString repair yields only marginal gains for simple perturbations and can degrade performance for semantic ones.

What carries the argument

Four categories of prompt perturbations (DocString, function name, syntax, and format) applied to measure robustness degradation in code generation tasks across multiple languages.

If this is right

Increasing model size alone is unlikely to solve reliability problems in code generation.
Changes that affect meaning in a prompt can damage output quality as much as changes that affect code structure.
Post-hoc prompt repairs using another LLM provide limited protection and can introduce new errors.
Language-specific differences affect how much a given perturbation hurts performance.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Prompt design guidelines that avoid semantic drift could raise effective reliability more than larger models.
Robustness might need to be trained into the model rather than fixed at inference time.
Collecting logs of real prompt variations from users would let researchers test whether the four categories capture the main risks.

Load-bearing premise

The four chosen perturbation categories and the specific changes inside them stand in for the variations that actually occur when developers use these models.

What would settle it

Measure the same models on a collection of genuine developer-written prompts that contain natural variations and check whether the performance drops match those seen with the artificial perturbations.

read the original abstract

Large language models have gained significant traction and popularity in recent times, extending their usage to code-generation tasks. While this field has garnered considerable attention, the exploration of testing and evaluating the robustness of code generation models remains an ongoing endeavor. Previous studies have primarily focused on code generation models specifically for the Python language, overlooking other widely used programming languages. In this work, we conduct a comprehensive comparative analysis to assess the robustness performance of several prominent code generation models and investigate whether robustness can be improved by repairing perturbed docstrings using an LLM. Furthermore, we investigate how their performance varies across different programming languages. To accomplish this, we introduce perturbations in four key areas of the prompt: DocString, function name, syntax, and format. We have compiled and released a dedicated dataset for this purpose. Our results show that all models consistently degrade under perturbations across all three languages, but vary in magnitude depending on the language and perturbation type. Larger model size does not reliably improve robustness, and semantic perturbations prove at least as disruptive as syntactic ones. Our LLM-based docString repair yields only marginal gains for simple perturbations and can degrade performance for semantic ones, highlighting the limits of prompt-level mitigation.

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

Comparative multi-language robustness results with a released dataset, though perturbation realism is a question.

read the letter

The main thing to know is that this work compares robustness of code LLMs across Python, Java, and C++ using prompt perturbations in docstrings, function names, syntax, and format, and they released the dataset. It extends earlier Python-only studies with the same basic approach and reports consistent performance drops that vary by language and perturbation type. Larger models do not reliably improve robustness, semantic changes hurt at least as much as syntactic ones, and LLM-based docstring repair gives only marginal help for simple cases while sometimes hurting semantic ones. Releasing the dataset stands out as a concrete positive for anyone who wants to build on the numbers. The paper does a decent job running the cross-language comparisons and making the data available so others can check or extend it. The soft spots center on the perturbations themselves. The four categories appear chosen by the authors without reported grounding in usage logs, user studies, or distributions from real developer prompts, so the observed degradation patterns may not generalize beyond this constructed set. The abstract also gives no details on statistical tests, error bars, or exact metrics, which leaves the strength of the trends harder to judge until the full results are examined. This paper is for researchers in software engineering and AI focused on code generation evaluation. A reader interested in prompt sensitivity data across languages would get some value from the comparisons and the public resource. I would send it for peer review. The multi-language angle and released dataset add usable evidence even if the test cases need better motivation.

Referee Report

2 major / 2 minor

Summary. The paper conducts a comparative analysis of the robustness of prominent LLM code generation models across three programming languages by introducing four categories of perturbations (DocString, function name, syntax, and format) to prompts. It releases a dedicated dataset, evaluates performance degradation, examines variation by language and perturbation type, tests whether larger model size improves robustness, compares semantic vs. syntactic perturbations, and assesses LLM-based docstring repair as a mitigation strategy. Key claims are that all models degrade consistently but with language- and type-dependent magnitude, scale does not reliably help, semantic perturbations are at least as disruptive as syntactic ones, and docstring repair yields only marginal or negative gains.

Significance. If the perturbations are representative, the work provides a useful multi-language extension beyond prior Python-only robustness studies, contributes an open dataset for reproducibility, and offers empirical evidence on the limits of scale and prompt-level fixes in code generation. The empirical design with released data supports further investigation even if specific claims require refinement.

major comments (2)

[§3] §3 (Perturbation Categories): The four perturbation categories and their specific transformations are presented as author-chosen without validation against real-world developer prompt distributions, usage logs, or user studies. Because the central claims about consistent degradation patterns, language-dependent variation, and the relative impact of semantic vs. syntactic changes rest on these perturbations serving as realistic proxies, the lack of grounding is load-bearing for generalizability.
[Results section] Results section: The abstract and evaluation report consistent degradation trends across models and languages, yet provide no details on statistical tests, error bars, exact pass@k or other metrics, or data exclusion criteria. This makes it difficult to evaluate the reliability and magnitude of the reported language- and type-dependent variations and the claim that larger models do not reliably improve robustness.

minor comments (2)

[Abstract] Clarify the precise three languages evaluated and list the specific models (with sizes) in the abstract and introduction for immediate context.
[Dataset] Add a short table or paragraph summarizing dataset statistics (number of functions per language, perturbation counts) to improve readability.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback and the recommendation for major revision. We address each major comment below with specific plans for revision where appropriate, while noting limitations that cannot be fully resolved within the current study scope.

read point-by-point responses

Referee: [§3] §3 (Perturbation Categories): The four perturbation categories and their specific transformations are presented as author-chosen without validation against real-world developer prompt distributions, usage logs, or user studies. Because the central claims about consistent degradation patterns, language-dependent variation, and the relative impact of semantic vs. syntactic changes rest on these perturbations serving as realistic proxies, the lack of grounding is load-bearing for generalizability.

Authors: We agree that the perturbations were selected based on common prompt variations discussed in prior code generation literature rather than direct empirical validation from developer logs or user studies. This choice was made to enable controlled comparison across languages while covering both semantic (docstring, function name) and syntactic (syntax, format) aspects. We cannot retroactively conduct new user studies or access proprietary usage logs for this work. In the revision, we will expand Section 3 with additional justification tied to existing studies on prompt sensitivity, and we will add an explicit limitations paragraph acknowledging that these serve as representative proxies rather than exhaustive real-world distributions. This will better contextualize the generalizability of the degradation patterns and language-dependent variations. revision: partial
Referee: [Results section] Results section: The abstract and evaluation report consistent degradation trends across models and languages, yet provide no details on statistical tests, error bars, exact pass@k or other metrics, or data exclusion criteria. This makes it difficult to evaluate the reliability and magnitude of the reported language- and type-dependent variations and the claim that larger models do not reliably improve robustness.

Authors: We acknowledge that the current presentation of results lacks sufficient methodological detail for full reproducibility and statistical assessment. The manuscript reports pass@1 as the primary metric with degradation trends, but does not include error bars, significance tests, or explicit exclusion rules. In the revised version, we will add: (1) details on statistical tests (e.g., paired comparisons with p-values for key differences), (2) error bars or confidence intervals on relevant figures, (3) clarification that pass@1 is the main reported metric with any additional k values if computed, and (4) a description of data filtering criteria (e.g., ensuring valid function signatures and excluding malformed perturbations). These additions will strengthen evaluation of the magnitude of variations and the model-size robustness claims. revision: yes

Circularity Check

0 steps flagged

Empirical evaluation with released dataset exhibits no circularity

full rationale

This is an empirical study that defines four perturbation categories, constructs and releases a dataset, runs models on perturbed prompts across languages, and reports observed degradation patterns. No equations, fitted parameters, predictions derived from prior fits, or self-citation chains appear in the abstract or described methodology. The central claims rest on direct experimental outcomes rather than reducing by construction to the paper's own inputs. The unvalidated representativeness of the chosen perturbations is a potential external-validity concern but does not create circularity in any derivation.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Empirical study with no mathematical derivations, free parameters, or new theoretical entities. Claims rest on experimental design choices and data collection rather than axioms or postulates.

pith-pipeline@v0.9.0 · 5737 in / 1145 out tokens · 48355 ms · 2026-05-22T19:17:04.439797+00:00 · methodology

discussion (0)

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We introduce perturbations in four key areas of the prompt: DocString, function name, syntax, and format... Our results show that all models consistently degrade under perturbations across all three languages

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