CETBench: A Novel Dataset constructed via Transformations over Programs for Benchmarking LLMs for Code-Equivalence Checking

• URL: http://arxiv.org/abs/2506.04019v1
• Date: Wed, 04 Jun 2025 14:47:14 GMT
• Title: CETBench: A Novel Dataset constructed via Transformations over Programs for Benchmarking LLMs for Code-Equivalence Checking
• Authors: Neeva Oza, Ishaan Govil, Parul Gupta, Dinesh Khandelwal, Dinesh Garg, Parag Singla,
• Abstract summary: We present CETBench - Code Equivalence with Transformations Benchmark, constructed via a repository of programs.<n>Each instance in our dataset is obtained by taking a pair of programs in the repository and applying a random series of pre-defined code transformations.<n>Our analysis reveals a surprising finding that very simple code transformations in the underlying pair of programs can result in a significant drop in performance of SOTA LLMs.
• Score: 18.036870409436137
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: LLMs have been extensively used for the task of automated code generation. In this work, we examine the applicability of LLMs for the related but relatively unexplored task of code-equivalence checking, i.e., given two programs, whether they are functionally equivalent or not. This is an important problem since benchmarking code equivalence can play a critical role in evaluating LLM capabilities for tasks such as code re-writing and code translation. Towards this end, we present CETBench - Code Equivalence with Transformations Benchmark, constructed via a repository of programs, where two programs in the repository may be solving the same or different tasks. Each instance in our dataset is obtained by taking a pair of programs in the repository and applying a random series of pre-defined code transformations, resulting in (non-)equivalent pairs. Our analysis on this dataset reveals a surprising finding that very simple code transformations in the underlying pair of programs can result in a significant drop in performance of SOTA LLMs for the task of code-equivalence checking. To remedy this, we present a simple fine-tuning-based approach to boost LLM performance on the transformed pairs of programs. Our approach for dataset generation is generic, and can be used with repositories with varying program difficulty levels and allows for applying varying numbers as well as kinds of transformations. In our experiments, we perform ablations over the difficulty level of original programs, as well as the kind of transformations used in generating pairs for equivalence checking. Our analysis presents deep insights into the working of LLMs for the task of code-equivalence, and points to the fact that they may still be far from what could be termed as a semantic understanding of the underlying code.

Related papers

• Chain-of-Descriptions: Improving Code LLMs for VHDL Code Generation and Summarization [4.7966941517322725]
  Large Language Models (LLMs) have become widely used across diverse NLP tasks and domains.<n>LLMs show promise for tasks like Register-Transfer Level (RTL) code generation and summarization.<n>We propose Chain-of-Descriptions (CoDes) to enhance the performance of LLMs for VHDL code generation and summarization tasks.
  arXiv  Detail & Related papers  (2025-07-16T15:05:30Z)
• Evaluating Large Language Models on Non-Code Software Engineering Tasks [4.381476817430934]
  Large Language Models (LLMs) have demonstrated remarkable capabilities in code understanding and generation.<n>We present the first comprehensive benchmark, which we name Software Engineering Language Understanding' (SELU)<n>SELU covers classification, regression, Named Entity Recognition (NER) and Masked Language Modeling (MLM) targets, with data drawn from diverse sources.
  arXiv  Detail & Related papers  (2025-06-12T15:52:32Z)
• Program Semantic Inequivalence Game with Large Language Models [10.358176296850639]
  Large Language Models (LLMs) can achieve strong performance on everyday coding tasks, but they can fail on complex tasks that require non-trivial reasoning about program semantics.<n>In this work, we explore a method to synthetically generate code reasoning training data based on a semantic inequivalence game SInQ.<n>We prove that this setup enables theoretically unlimited improvement through self-play in the limit of infinite computational resources.
  arXiv  Detail & Related papers  (2025-05-02T20:03:35Z)
• EquiBench: Benchmarking Large Language Models' Understanding of Program Semantics via Equivalence Checking [55.81461218284736]
  EquiBench is a new benchmark for evaluating large language models (LLMs)<n>It determines whether two programs produce identical outputs for all possible inputs.<n>We evaluate 19 state-of-the-art LLMs and find that the best accuracies are 63.8% and 76.2%, only modestly above the 50% random baseline.
  arXiv  Detail & Related papers  (2025-02-18T02:54:25Z)
• SnipGen: A Mining Repository Framework for Evaluating LLMs for Code [51.07471575337676]
  Language Models (LLMs) are trained on extensive datasets that include code repositories.<n> evaluating their effectiveness poses significant challenges due to the potential overlap between the datasets used for training and those employed for evaluation.<n>We introduce SnipGen, a comprehensive repository mining framework designed to leverage prompt engineering across various downstream tasks for code generation.
  arXiv  Detail & Related papers  (2025-02-10T21:28:15Z)
• TRANSAGENT: An LLM-Based Multi-Agent System for Code Translation [16.46292795782835]
  Code translation is crucial for software migration, system ablation, and cross-platform development. Traditional rule-based methods rely on manually-written rules, which can be time-consuming and often result in less readable code. More recently, the advance of Large Language Models (LLMs) further boosts learning-based code translation. We propose a novel multi-agent system TRANSAGENT, which enhances LLM-based code translation by fixing the syntax errors and semantic errors.
  arXiv  Detail & Related papers  (2024-09-30T02:53:03Z)
• Case2Code: Scalable Synthetic Data for Code Generation [105.89741089673575]
  Large Language Models (LLMs) have shown outstanding breakthroughs in code generation.<n>Recent work improves code LLMs by training on synthetic data generated by some powerful LLMs.<n>We propose a textbfCase2Code task by exploiting the expressiveness and correctness of programs.
  arXiv  Detail & Related papers  (2024-07-17T11:35:00Z)
• An Empirical Study on Capability of Large Language Models in Understanding Code Semantics [4.638578225024275]
  Large Language Models for Code (code LLMs) have demonstrated remarkable performance across various software engineering (SE) tasks. This paper introduces EMPICA, a framework designed to evaluate the capabilities of code LLMs in understanding code semantics.
  arXiv  Detail & Related papers  (2024-07-04T03:40:58Z)
• CodecLM: Aligning Language Models with Tailored Synthetic Data [51.59223474427153]
  We introduce CodecLM, a framework for adaptively generating high-quality synthetic data for instruction-following abilities. We first encode seed instructions into metadata, which are concise keywords generated on-the-fly to capture the target instruction distribution. We also introduce Self-Rubrics and Contrastive Filtering during decoding to tailor data-efficient samples.
  arXiv  Detail & Related papers  (2024-04-08T21:15:36Z)
• Mutation-based Consistency Testing for Evaluating the Code Understanding Capability of LLMs [5.549095839198671]
  Large Language Models (LLMs) have shown remarkable capabilities in processing both natural and programming languages. We propose a novel method to assess the code understanding performance of LLMs, particularly focusing on subtle differences between code and its descriptions. We apply different types of code mutations, such as operator replacement and statement deletion, to generate inconsistent code-description pairs. We conduct a case study on the two popular LLMs, GPT-3.5 and GPT-4, using the state-of-the-art code generation benchmark, HumanEval-X.
  arXiv  Detail & Related papers  (2024-01-11T14:27:43Z)
• If LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code Empowers Large Language Models to Serve as Intelligent Agents [81.60906807941188]
  Large language models (LLMs) are trained on a combination of natural language and formal language (code) Code translates high-level goals into executable steps, featuring standard syntax, logical consistency, abstraction, and modularity.
  arXiv  Detail & Related papers  (2024-01-01T16:51:20Z)
• ML-Bench: Evaluating Large Language Models and Agents for Machine Learning Tasks on Repository-Level Code [76.84199699772903]
  ML-Bench is a benchmark rooted in real-world programming applications that leverage existing code repositories to perform tasks. To evaluate both Large Language Models (LLMs) and AI agents, two setups are employed: ML-LLM-Bench for assessing LLMs' text-to-code conversion within a predefined deployment environment, and ML-Agent-Bench for testing autonomous agents in an end-to-end task execution within a Linux sandbox environment.
  arXiv  Detail & Related papers  (2023-11-16T12:03:21Z)
• LEVER: Learning to Verify Language-to-Code Generation with Execution [64.36459105535]
  We propose LEVER, a simple approach to improve language-to-code generation by learning to verify the generated programs with their execution results. Specifically, we train verifiers to determine whether a program sampled from the LLMs is correct or not based on the natural language input, the program itself and its execution results. LEVER consistently improves over the base code LLMs(4.6% to 10.9% with code-davinci) and achieves new state-of-the-art results on all of them.
  arXiv  Detail & Related papers  (2023-02-16T18:23:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.