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arxiv: 2412.04590 · v2 · submitted 2024-12-05 · 💻 cs.SE

Specification-Driven Code Translation Powered by Large Language Models: How Far Are We?

Soumit Kanti Saha , Fazle Rabbi , Song Wang , Jinqiu Yang This is my paper

Pith reviewed 2026-05-23 07:33 UTC · model grok-4.3

classification 💻 cs.SE
keywords code translationlarge language modelsnatural language specificationprogramming language pairsLLM evaluationcode qualitysoftware engineeringfunctional correctness
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The pith

Natural language specifications alone do not improve LLM code translation performance, though combining them with source code yields gains in select language pairs.

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

The paper tests whether feeding an LLM a natural-language specification as an intermediate step helps translate code from one programming language to another. Experiments cover three existing datasets, five languages, and all 29 possible translation directions. Results indicate that an NL specification by itself produces no reliable gains over direct translation. Adding the specification to the source code improves accuracy for some pairs, especially when Python or C++ is the source, yet the improvement is not uniform across all pairs. The work also examines the functional correctness and common defects in the output code.

Core claim

Using NL-specification alone does not lead to performance improvements. When combined with source code, it provides gains in certain language pairs (notably with Python and C++ as source languages), while offering no consistent improvement overall. The study also supplies qualitative observations on defects that remain in the translated programs.

What carries the argument

NL-specification as an intermediate representation between source and target code in an LLM prompt.

If this is right

  • NL specification by itself is not a reliable booster for translation accuracy.
  • Pairing the specification with the original source code can raise success rates when the source language is Python or C++.
  • The benefit remains pair-dependent, so blanket adoption of the intermediate step is not justified by current evidence.
  • Quality analysis of outputs reveals recurring functional and syntactic defects that persist regardless of the prompt strategy.

Where Pith is reading between the lines

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

  • Direct source-to-target prompting may remain the stronger default strategy for most language pairs until better integration methods appear.
  • The observed source-language asymmetry suggests that future experiments should stratify results by source rather than treating all pairs symmetrically.
  • If the pattern holds, tool builders could add an optional NL-specification toggle that activates only for Python-to-other or C++-to-other translations.

Load-bearing premise

The three chosen datasets and the 29 language-pair permutations are representative enough to support general statements about the usefulness of NL specifications for code translation tasks.

What would settle it

Re-running the identical prompt templates and models on a fourth, independently collected dataset that spans the same five languages and finding consistent accuracy gains in every direction would contradict the claim of no overall improvement.

Figures

Figures reproduced from arXiv: 2412.04590 by Fazle Rabbi, Jinqiu Yang, Song Wang, Soumit Kanti Saha.

Figure 1
Figure 1. Figure 1: The workflow of our work. the target PLs according to their popularity (i.e., TIOBE index [24]), covering different paradigms (procedural, func￾tional, and object-oriented), and the availability of high-quality datasets. The selected PLs were C, C++, Go, Java, and Python. In Avatar, only Java and Python are available as source PL. The only available source PL in EvalPlus is Python. C. Subject LLMs As our f… view at source ↗
Figure 2
Figure 2. Figure 2: An example of source code and generated NL [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: How correct NL-specificationcan boost translation. [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: How incorrect NL-specifications deteriorate translation. [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Violin plot showing the distribution of issues per 1k non-commented lines of code for different methods. LIT: Lost in [PITH_FULL_IMAGE:figures/full_fig_p010_5.png] view at source ↗
read the original abstract

Large Language Models (LLMs) are increasingly being applied across various domains, including code-related tasks such as code translation. Previous studies have explored using LLMs for translating code between different programming languages. Since LLMs are more effective with natural language, using natural language as an intermediate representation in code translation tasks is an intuitively appealing approach. However, whether this benefit is general or highly context-dependent remains unclear. In this work, we investigate using NL-specification as an intermediate representation for code translation. We evaluate our method using three datasets, five popular programming languages, and 29 language pair permutations. Our results show that using NL-specification alone does not lead to performance improvements. However, when combined with source code, it provides gains in certain language pairs (notably with Python and C++ as source languages), while offering no consistent improvement overall. Besides analyzing the performance of code translation, we also investigate the quality of the translated code and provide insights into the issues present in the translated 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.

Referee Report

2 major / 2 minor

Summary. The manuscript presents an empirical study on using natural language (NL) specifications as an intermediate representation for code translation tasks with large language models (LLMs). The authors evaluate their approach across three datasets, five programming languages, and 29 language-pair permutations. Key findings indicate that NL-specification alone does not improve translation performance, but combining it with the source code can provide gains in specific language pairs, particularly when Python or C++ is the source language, though improvements are not consistent overall. The paper also analyzes the quality of the generated translations and identifies common issues.

Significance. If the results hold under more rigorous controls, the work provides useful empirical evidence that NL specifications are not a general-purpose enhancer for LLM-based code translation but may offer targeted benefits in select source-language settings. The multi-dataset, multi-language design is a strength that allows for comparative analysis across permutations, and the additional focus on translation quality issues (beyond accuracy metrics) adds practical value for the field.

major comments (2)
  1. [§4 and §5] §4 (Experimental Setup) and §5 (Results): No statistical tests, confidence intervals, error bars, or controls for prompt variation are described, leaving the reported gains for specific pairs (e.g., Python/C++ sources) and the 'no consistent improvement' claim without quantified reliability; this directly affects the soundness of the central empirical conclusions.
  2. [§3 and §5] §3 (Datasets) and §5: The three datasets and 29 permutations are presented without explicit selection criteria, domain coverage, size statistics, or justification of representativeness for syntax divergence and task complexity; this makes the generalization that NL-spec utility is 'highly context-dependent' load-bearing on unverified sampling assumptions.
minor comments (2)
  1. [Abstract and §1] Abstract and §1: Dataset names and basic characteristics (sizes, domains) are omitted, which would improve readability and allow immediate assessment of scope.
  2. [§6] §6 (Discussion): The analysis of translation issues could benefit from more precise categorization or examples tied back to the language-pair results.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our empirical study. The comments highlight important opportunities to strengthen the statistical rigor and dataset justification, which we address point by point below with plans for revision.

read point-by-point responses

  1. Referee: [§4 and §5] §4 (Experimental Setup) and §5 (Results): No statistical tests, confidence intervals, error bars, or controls for prompt variation are described, leaving the reported gains for specific pairs (e.g., Python/C++ sources) and the 'no consistent improvement' claim without quantified reliability; this directly affects the soundness of the central empirical conclusions.

    Authors: We agree that the lack of statistical tests, confidence intervals, error bars, and prompt variation controls weakens the reliability of the reported gains and the 'no consistent improvement' conclusion. In the revised manuscript, we will add paired statistical tests (e.g., McNemar's test or Wilcoxon signed-rank) for accuracy differences, report 95% confidence intervals, include error bars on performance figures, and average results over multiple prompt templates with variance analysis to quantify reliability. revision: yes

  2. Referee: [§3 and §5] §3 (Datasets) and §5: The three datasets and 29 permutations are presented without explicit selection criteria, domain coverage, size statistics, or justification of representativeness for syntax divergence and task complexity; this makes the generalization that NL-spec utility is 'highly context-dependent' load-bearing on unverified sampling assumptions.

    Authors: The three datasets are established benchmarks commonly used in code translation research, chosen to span multiple languages and task complexities. However, we acknowledge the need for explicit justification. In the revision, we will expand §3 with selection criteria (popularity, public availability, and coverage of syntax differences), domain coverage details, size statistics per language pair, and quantitative measures of syntax divergence to better ground the context-dependent claims. revision: yes

Circularity Check

0 steps flagged

Empirical benchmark study with no derivation chain or fitted parameters

full rationale

This is a pure empirical evaluation paper that measures LLM translation performance on external datasets (three chosen corpora, five languages, 29 pairs) under different input conditions (NL-spec alone vs. source+spec). No equations, ansatzes, uniqueness theorems, or parameter-fitting steps exist that could reduce a claimed result to its own inputs by construction. Claims about 'no consistent improvement' are direct observations from the runs, not predictions derived from prior self-citations or self-definitions. The representativeness concern is a validity issue, not a circularity issue.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on standard empirical software engineering assumptions about dataset representativeness and metric validity rather than new axioms or invented entities.

axioms (1)
  • domain assumption The selected datasets and language pairs sufficiently represent real-world code translation scenarios.
    Invoked to allow generalization from the 29 permutations to broader conclusions about the technique.

pith-pipeline@v0.9.0 · 5707 in / 1199 out tokens · 20130 ms · 2026-05-23T07:33:49.396473+00:00 · methodology

discussion (0)

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Forward citations

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