Imagen de Portada

CPP-UT-Bench: Can LLMs Write Complex Unit Tests in C++?

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Bhargava, Vaishnavi, Ghosh, Rajat, Dutta, Debojyoti
Formato:	Preprint
Publicado:	2024
Materias:	Software Engineering Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

SWE-Tester: Training Open-Source LLMs for Issue Reproduction in Real-World Repositories
por: Soni, Aditya Bharat, et al.
Publicado: (2026)

Go-UT-Bench: A Fine-Tuning Dataset for LLM-Based Unit Test Generation in Go
por: Pipalani, Yashshi, et al.
Publicado: (2025)

RANGER -- Repository-Level Agent for Graph-Enhanced Retrieval
por: Shah, Pratik, et al.
Publicado: (2025)

A Multi-Agent Framework for Stateful Inference-Time Search
por: Lalan, Arshika, et al.
Publicado: (2025)

CR-Bench: Evaluating the Real-World Utility of AI Code Review Agents
por: Pereira, Kristen, et al.
Publicado: (2026)

KernelBench: Can LLMs Write Efficient GPU Kernels?
por: Ouyang, Anne, et al.
Publicado: (2025)

AssertionBench: A Benchmark to Evaluate Large-Language Models for Assertion Generation
por: Pulavarthi, Vaishnavi, et al.
Publicado: (2024)

TDD-Bench Verified: Can LLMs Generate Tests for Issues Before They Get Resolved?
por: Ahmed, Toufique, et al.
Publicado: (2024)

BAR Conjecture: the Feasibility of Inference Budget-Constrained LLM Services with Authenticity and Reasoning
por: Zhou, Jinan, et al.
Publicado: (2025)

Predictive Scaling Laws for Efficient GRPO Training of Large Reasoning Models
por: Nimmaturi, Datta, et al.
Publicado: (2025)

OSS-Bench: Benchmark Generator for Coding LLMs
por: Jiang, Yuancheng, et al.
Publicado: (2025)

ThrowBench: Benchmarking LLMs by Predicting Runtime Exceptions
por: Prenner, Julian Aron, et al.
Publicado: (2025)

RM -RF: Reward Model for Run-Free Unit Test Evaluation
por: Bruches, Elena, et al.
Publicado: (2026)

PrismaDV: Automated Task-Aware Data Unit Test Generation
por: Chen, Hao, et al.
Publicado: (2026)

Can LLMs Find Bugs in Code? An Evaluation from Beginner Errors to Security Vulnerabilities in Python and C++
por: Mhatre, Akshay, et al.
Publicado: (2025)

Reinforcement Learning from Automatic Feedback for High-Quality Unit Test Generation
por: Steenhoek, Benjamin, et al.
Publicado: (2024)

Reinforcement Learning from Automatic Feedback for High-Quality Unit Test Generation
por: Steenhoek, Benjamin, et al.
Publicado: (2023)

Can LLMs Reason Like Automated Theorem Provers for Rust Verification? VCoT-Bench: Evaluating via Verification Chain of Thought
por: Xie, Zichen, et al.
Publicado: (2026)

SWE-Lancer: Can Frontier LLMs Earn $1 Million from Real-World Freelance Software Engineering?
por: Miserendino, Samuel, et al.
Publicado: (2025)

Enabling Global, Human-Centered Explanations for LLMs:From Tokens to Interpretable Code and Test Generation
por: Khati, Dipin, et al.
Publicado: (2025)

CoCoNUT: Structural Code Understanding does not fall out of a tree
por: Beger, Claas, et al.
Publicado: (2025)

PostTrainBench: Can LLM Agents Automate LLM Post-Training?
por: Rank, Ben, et al.
Publicado: (2026)

Action Shapley: A Training Data Selection Metric for World Model in Reinforcement Learning
por: Ghosh, Rajat, et al.
Publicado: (2026)

RepairBench: Leaderboard of Frontier Models for Program Repair
por: Silva, André, et al.
Publicado: (2024)

EnvBench: A Benchmark for Automated Environment Setup
por: Eliseeva, Aleksandra, et al.
Publicado: (2025)

SWT-Bench: Testing and Validating Real-World Bug-Fixes with Code Agents
por: Mündler, Niels, et al.
Publicado: (2024)

Operational Robustness of LLMs on Code Generation
por: Paul, Debalina Ghosh, et al.
Publicado: (2026)

TeleResilienceBench: Quantifying Resilience for LLM Reasoning in Telecommunications
por: Gajjar, Pranshav, et al.
Publicado: (2026)

AICD Bench: A Challenging Benchmark for AI-Generated Code Detection
por: Orel, Daniil, et al.
Publicado: (2026)

Can Search-Based Testing with Pareto Optimization Effectively Cover Failure-Revealing Test Inputs?
por: Sorokin, Lev, et al.
Publicado: (2024)

Parameter-Efficient Fine-Tuning of Large Language Models for Unit Test Generation: An Empirical Study
por: Storhaug, André, et al.
Publicado: (2024)

InfiBench: Evaluating the Question-Answering Capabilities of Code Large Language Models
por: Li, Linyi, et al.
Publicado: (2024)

CoDocBench: A Dataset for Code-Documentation Alignment in Software Maintenance
por: Pai, Kunal, et al.
Publicado: (2025)

MobileDev-Bench: A Benchmark for Issue Resolution in Mobile Application Development
por: Fakorede, Moshood A., et al.
Publicado: (2026)

Can LLMs Generate Architectural Design Decisions? -An Exploratory Empirical study
por: Dhar, Rudra, et al.
Publicado: (2024)

CodeTaste: Can LLMs Generate Human-Level Code Refactorings?
por: Thillen, Alex, et al.
Publicado: (2026)

Learning to Generate Unit Tests for Automated Debugging
por: Prasad, Archiki, et al.
Publicado: (2025)

CRUST-Bench: A Comprehensive Benchmark for C-to-safe-Rust Transpilation
por: Khatry, Anirudh, et al.
Publicado: (2025)

IDE-Bench: Evaluating Large Language Models as IDE Agents on Real-World Software Engineering Tasks
por: Mateega, Spencer, et al.
Publicado: (2026)

Disproving Program Equivalence with LLMs
por: Allamanis, Miltiadis, et al.
Publicado: (2025)