Copertina

What You See Is What You Get: Attention-based Self-guided Automatic Unit Test Generation

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Yin, Xin, Ni, Chao, Xu, Xiaodan, Yang, Xiaohu
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Software Engineering
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

What You See Is Not Always What You Get: Evaluating GPT's Comprehension of Source Code
di: Wen, Jiawen, et al.
Pubblicazione: (2024)

What You Need is What You Get: Theory of Mind for an LLM-Based Code Understanding Assistant
di: Richards, Jonan, et al.
Pubblicazione: (2024)

What You Use is What You Get: Unforced Errors in Studying Cultural Aspects in Agile Software Development
di: Neumann, Michael, et al.
Pubblicazione: (2024)

Enhancing LLM's Ability to Generate More Repository-Aware Unit Tests Through Precise Contextual Information Injection
di: Yin, Xin, et al.
Pubblicazione: (2025)

Improving the Ability of Pre-trained Language Model by Imparting Large Language Model's Experience
di: Yin, Xin, et al.
Pubblicazione: (2024)

Detecting LLM-generated Code with Subtle Modification by Adversarial Training
di: Yin, Xin, et al.
Pubblicazione: (2025)

CasModaTest: A Cascaded and Model-agnostic Self-directed Framework for Unit Test Generation
di: Ni, Chao, et al.
Pubblicazione: (2024)

What You See Is What It Does: A Structural Pattern for Legible Software
di: Meng, Eagon, et al.
Pubblicazione: (2025)

Automated Unit Test Refactoring
di: Gao, Yi, et al.
Pubblicazione: (2024)

Learning to Align Human Code Preferences
di: Yin, Xin, et al.
Pubblicazione: (2025)

ThinkRepair: Self-Directed Automated Program Repair
di: Yin, Xin, et al.
Pubblicazione: (2024)

Navigating the Labyrinth: Path-Sensitive Unit Test Generation with Large Language Models
di: Liao, Dianshu, et al.
Pubblicazione: (2025)

Automatically Generating Single-Responsibility Unit Tests
di: Galindo-Gutierrez, Geraldine
Pubblicazione: (2025)

Distinguishing LLM-generated from Human-written Code by Contrastive Learning
di: Xu, Xiaodan, et al.
Pubblicazione: (2024)

What Do You Mean by Memory? When Engineers Are Lost in the Maze of Complexity
di: Kudrjavets, Gunnar, et al.
Pubblicazione: (2023)

Learning-based Models for Vulnerability Detection: An Extensive Study
di: Ni, Chao, et al.
Pubblicazione: (2024)

How Toxic Can You Get? Search-based Toxicity Testing for Large Language Models
di: Corbo, Simone, et al.
Pubblicazione: (2025)

Multitask-based Evaluation of Open-Source LLM on Software Vulnerability
di: Yin, Xin, et al.
Pubblicazione: (2024)

Rectifier: Code Translation with Corrector via LLMs
di: Yin, Xin, et al.
Pubblicazione: (2024)

What Makes a Good TODO Comment?
di: Wang, Haoye, et al.
Pubblicazione: (2025)

You Don't Know Until You Click:Automated GUI Testing for Production-Ready Software Evaluation
di: Bian, Yutong, et al.
Pubblicazione: (2025)

Testing Autonomous Driving Systems -- What Really Matters and What Doesn't
di: Li, Changwen, et al.
Pubblicazione: (2025)

Fuzzing with Agents? Generators Are All You Need
di: Vikram, Vasudev, et al.
Pubblicazione: (2026)

Vulnerability-Triggering Test Case Generation from Third-Party Libraries
di: Gao, Yi, et al.
Pubblicazione: (2024)

No More Manual Tests? Evaluating and Improving ChatGPT for Unit Test Generation
di: Yuan, Zhiqiang, et al.
Pubblicazione: (2023)

Test smells in LLM-Generated Unit Tests
di: Ouédraogo, Wendkûuni C., et al.
Pubblicazione: (2024)

LLM-based Unit Test Generation via Property Retrieval
di: Zhang, Zhe, et al.
Pubblicazione: (2024)

Search-based Hyperparameter Tuning for Python Unit Test Generation
di: Lukasczyk, Stephan, et al.
Pubblicazione: (2025)

What You Trust Is Insecure: Demystifying How Developers (Mis)Use Trusted Execution Environments in Practice
di: Niu, Yuqing, et al.
Pubblicazione: (2025)

SeeAction: Towards Reverse Engineering How-What-Where of HCI Actions from Screencasts for UI Automation
di: Zhao, Dehai, et al.
Pubblicazione: (2025)

Synthesizing File-Level Data for Unit Test Generation with Chain-of-Thoughts via Self-Debugging
di: Hua, Ziyue, et al.
Pubblicazione: (2026)

On the Evaluation of Large Language Models in Unit Test Generation
di: Yang, Lin, et al.
Pubblicazione: (2024)

DEFT: Differentiable Automatic Test Pattern Generation
di: Li, Wei, et al.
Pubblicazione: (2025)

A Preference-Driven Methodology for High-Quality Solidity Code Generation
di: Peng, Zhiyuan, et al.
Pubblicazione: (2025)

Names Are All You Need: Effective and Safe Regression Test Selection for Python
di: Wang, You, et al.
Pubblicazione: (2026)

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

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

Large Language Models for Unit Test Generation: Achievements, Challenges, and Opportunities
di: Chu, Bei, et al.
Pubblicazione: (2025)

Test-based Patch Clustering for Automatically-Generated Patches Assessment
di: Martinez, Matias, et al.
Pubblicazione: (2022)

Mutation-Guided Unit Test Generation with a Large Language Model
di: Wang, Guancheng, et al.
Pubblicazione: (2025)