arXiv subjects

Soumit Kanti Saha

Publications and source records attributed to Soumit Kanti Saha.

4 recordsLinked to original sources

arXiv2026

BabelCoder: Agentic Code Translation with Specification Alignment

As software systems evolve, developers increasingly work across multiple programming languages and often face the need to migrate code from one language to another. While automatic code translation offers a promising solution, it has long remained a challenging task. Recent advancements in Large Language Models (LLMs) have shown potential for this task, yet existing approaches remain limited in accuracy and fail to effectively leverage contextual and structural cues within the code. Prior work has explored translation and repair mechanisms, but lacks a structured, agentic framework where multiple specialized agents collaboratively improve translation quality. In this work, we introduce BabelCoder, an agentic framework that performs code translation by decomposing the task into specialized agents for translation, testing, and refinement, each responsible for a specific aspect such as generating code, validating correctness, or repairing errors. We evaluate BabelCoder on four benchmark datasets and compare it against four state-of-the-art baselines. BabelCoder outperforms existing methods by 0.5%-13.5% in 94% of cases, achieving an average accuracy of 94.16%.

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arXiv2026

Beyond Translation Accuracy: Addressing False Failures in LLM-Based Code Translation

Large Language Models (LLMs) have achieved remarkable success in automated code translation. While prior work has focused on improving translation accuracy through advanced prompting and iterative repair, the reliability of the underlying evaluation frameworks has received less attention. In this paper, we demonstrate that a significant number of reported failures in code translation are not due to incorrect logic, but rather evaluation-induced errors stemming from improper compilation flags, missing library links, and unconfigured runtime environments. We conduct a large-scale empirical study across five programming languages (C, C++, Java, Python, Go) and three benchmarks (Avatar, CodeNet, EvalPlus), covering 6,164 translations generated by GPT-4o, DeepSeek-Coder, and Magicoder. Our analysis identifies and categorizes common false negatives, distinguishing pipeline-induced failures that affect any model from model-dependent behaviors that vary across LLMs. Our findings highlight the necessity for transparent, configuration-aware evaluation standards to accurately assess progress in LLM-based code translation.

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arXiv2026

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

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.

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arXiv2025

Demystifying Errors in LLM Reasoning Traces: An Empirical Study of Code Execution Simulation

Understanding a program's runtime reasoning behavior, meaning how intermediate states and control flows lead to final execution results, is essential for reliable code generation, debugging, and automated reasoning. Although large language models (LLMs) can accurately predict program outputs, most prior work has focused on output accuracy and performance, treating reasoning as a black box. As a result, little is known about the structure or failure modes of their reasoning traces. To address this gap, we conduct the first empirical study on runtime behavior inference with reasoning LLMs, aiming to uncover and characterize errors in their reasoning traces. We curate a benchmark from HumanEval Plus and LiveCodeBench, containing 427 code snippets. For each snippet, we test three input types: regular, edge, and invalid. Twelve input values are selected per snippet, each paired with its ground-truth execution result. We evaluate four state-of-the-art reasoning LLMs. Our results show that these models reach accuracies between 85 percent and 98 percent across input types. We also analyze the produced reasoning traces and develop a taxonomy with nine categories of inference errors. Finally, we explore tool-augmented reasoning. Using failures in the Computation Errors category as a case study, our experiments show that this approach corrects 58 percent of such errors, demonstrating the potential of tool support for improving LLM reasoning.

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