Navigating the Labyrinth: Path-Sensitive Unit Test Generation with Large Language Models
Fuente:
arXiv
Saved in:
| Main Authors: | Liao, Dianshu, Yin, Xin, Pan, Shidong, Ni, Chao, Xing, Zhenchang, Sun, Xiaoyu |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
A Comparative Study of Android Performance Issues in Real-world Applications and Literature
by: Liao, Dianshu, et al.
Published: (2024)
by: Liao, Dianshu, et al.
Published: (2024)
A Solution toward Transparent and Practical AI Regulation: Privacy Nutrition Labels for Open-source Generative AI-based Applications
by: Si, Meixue, et al.
Published: (2024)
by: Si, Meixue, et al.
Published: (2024)
A^3-CodGen: A Repository-Level Code Generation Framework for Code Reuse with Local-Aware, Global-Aware, and Third-Party-Library-Aware
by: Liao, Dianshu, et al.
Published: (2023)
by: Liao, Dianshu, et al.
Published: (2023)
Deployability-Centric Infrastructure-as-Code Generation: Fail, Learn, Refine, and Succeed through LLM-Empowered DevOps Simulation
by: Zhang, Tianyi, et al.
Published: (2025)
by: Zhang, Tianyi, et al.
Published: (2025)
A Large-scale Investigation of Semantically Incompatible APIs behind Compatibility Issues in Android Apps
by: Pan, Shidong, et al.
Published: (2024)
by: Pan, Shidong, et al.
Published: (2024)
Towards Context-aware Mobile Privacy Notice: Implementation of A Deployable Contextual Privacy Policies Generator
by: Gong, Haochen, et al.
Published: (2025)
by: Gong, Haochen, et al.
Published: (2025)
When AI Takes the Wheel: Security Analysis of Framework-Constrained Program Generation
by: Liu, Yue, et al.
Published: (2025)
by: Liu, Yue, et al.
Published: (2025)
Leveraging Large Language Models for Enhancing the Understandability of Generated Unit Tests
by: Deljouyi, Amirhossein, et al.
Published: (2024)
by: Deljouyi, Amirhossein, et al.
Published: (2024)
What You See Is What You Get: Attention-based Self-guided Automatic Unit Test Generation
by: Yin, Xin, et al.
Published: (2024)
by: Yin, Xin, et al.
Published: (2024)
A System for Automated Unit Test Generation Using Large Language Models and Assessment of Generated Test Suites
by: Lops, Andrea, et al.
Published: (2024)
by: Lops, Andrea, et al.
Published: (2024)
"I Don't Use AI for Everything": Exploring Utility, Attitude, and Responsibility of AI-empowered Tools in Software Development
by: Pan, Shidong, et al.
Published: (2024)
by: Pan, Shidong, et al.
Published: (2024)
Privacy Bills of Materials: A Transparent Privacy Information Inventory for Collaborative Privacy Notice Generation in Mobile App Development
by: Tao, Zhen, et al.
Published: (2025)
by: Tao, Zhen, et al.
Published: (2025)
Evaluating Large Language Models for the Generation of Unit Tests with Equivalence Partitions and Boundary Values
by: Rodríguez, Martín, et al.
Published: (2025)
by: Rodríguez, Martín, et al.
Published: (2025)
To Be Forgotten or To Be Fair: Unveiling Fairness Implications of Machine Unlearning Methods
by: Zhang, Dawen, et al.
Published: (2023)
by: Zhang, Dawen, et al.
Published: (2023)
HFuzzer: Testing Large Language Models for Package Hallucinations via Phrase-based Fuzzing
by: Zhao, Yukai, et al.
Published: (2025)
by: Zhao, Yukai, et al.
Published: (2025)
Impact of Code Context and Prompting Strategies on Automated Unit Test Generation with Modern General-Purpose Large Language Models
by: Walczak, Jakub, et al.
Published: (2025)
by: Walczak, Jakub, et al.
Published: (2025)
Enhancing LLM's Ability to Generate More Repository-Aware Unit Tests Through Precise Contextual Information Injection
by: Yin, Xin, et al.
Published: (2025)
by: Yin, Xin, et al.
Published: (2025)
On the Evaluation of Large Language Models in Unit Test Generation
by: Yang, Lin, et al.
Published: (2024)
by: Yang, Lin, et al.
Published: (2024)
Learning to Align Human Code Preferences
by: Yin, Xin, et al.
Published: (2025)
by: Yin, Xin, et al.
Published: (2025)
Towards Responsible Generative AI: A Reference Architecture for Designing Foundation Model based Agents
by: Lu, Qinghua, et al.
Published: (2023)
by: Lu, Qinghua, et al.
Published: (2023)
Domain-constrained Synthesis of Inconsistent Key Aspects in Textual Vulnerability Descriptions
by: Han, Linyi, et al.
Published: (2025)
by: Han, Linyi, et al.
Published: (2025)
Improving the Ability of Pre-trained Language Model by Imparting Large Language Model's Experience
by: Yin, Xin, et al.
Published: (2024)
by: Yin, Xin, et al.
Published: (2024)
A First Look at Privacy Risks of Android Task-executable Voice Assistant Applications
by: Pan, Shidong, et al.
Published: (2025)
by: Pan, Shidong, et al.
Published: (2025)
Parameter-Efficient Fine-Tuning of Large Language Models for Unit Test Generation: An Empirical Study
by: Storhaug, André, et al.
Published: (2024)
by: Storhaug, André, et al.
Published: (2024)
Do Chase Your Tail! Missing Key Aspects Augmentation in Textual Vulnerability Descriptions of Long-tail Software through Feature Inference
by: Han, Linyi, et al.
Published: (2024)
by: Han, Linyi, et al.
Published: (2024)
A Layered Architecture for Developing and Enhancing Capabilities in Large Language Model-based Software Systems
by: Zhang, Dawen, et al.
Published: (2024)
by: Zhang, Dawen, et al.
Published: (2024)
Identifying and Mitigating API Misuse in Large Language Models
by: Zhuo, Terry Yue, et al.
Published: (2025)
by: Zhuo, Terry Yue, et al.
Published: (2025)
Domain Adaptation for Code Model-based Unit Test Case Generation
by: Shin, Jiho, et al.
Published: (2023)
by: Shin, Jiho, et al.
Published: (2023)
Unit Testing in ASP Revisited: Language and Test-Driven Development Environment
by: Amendola, Giovanni, et al.
Published: (2024)
by: Amendola, Giovanni, et al.
Published: (2024)
CasModaTest: A Cascaded and Model-agnostic Self-directed Framework for Unit Test Generation
by: Ni, Chao, et al.
Published: (2024)
by: Ni, Chao, et al.
Published: (2024)
Mutation-Guided Unit Test Generation with a Large Language Model
by: Wang, Guancheng, et al.
Published: (2025)
by: Wang, Guancheng, et al.
Published: (2025)
Large Language Models for Unit Test Generation: Achievements, Challenges, and Opportunities
by: Chu, Bei, et al.
Published: (2025)
by: Chu, Bei, et al.
Published: (2025)
Refactoring to Pythonic Idioms: A Hybrid Knowledge-Driven Approach Leveraging Large Language Models
by: Zhang, Zejun, et al.
Published: (2024)
by: Zhang, Zejun, et al.
Published: (2024)
Explore-Construct-Filter: An Automated Framework for Rich and Reliable API Knowledge Graph Construction
by: Sun, Yanbang, et al.
Published: (2025)
by: Sun, Yanbang, et al.
Published: (2025)
Clarifying Semantics of In-Context Examples for Unit Test Generation
by: Yang, Chen, et al.
Published: (2025)
by: Yang, Chen, et al.
Published: (2025)
Automated Unit Test Refactoring
by: Gao, Yi, et al.
Published: (2024)
by: Gao, Yi, et al.
Published: (2024)
Large Language Models as Test Case Generators: Performance Evaluation and Enhancement
by: Li, Kefan, et al.
Published: (2024)
by: Li, Kefan, et al.
Published: (2024)
A Tool for Generating Exceptional Behavior Tests With Large Language Models
by: Zhong, Linghan, et al.
Published: (2025)
by: Zhong, Linghan, et al.
Published: (2025)
exLong: Generating Exceptional Behavior Tests with Large Language Models
by: Zhang, Jiyang, et al.
Published: (2024)
by: Zhang, Jiyang, et al.
Published: (2024)
Evaluation-Driven Development and Operations of LLM Agents: A Process Model and Reference Architecture
by: Xia, Boming, et al.
Published: (2024)
by: Xia, Boming, et al.
Published: (2024)
Similar Items
-
A Comparative Study of Android Performance Issues in Real-world Applications and Literature
by: Liao, Dianshu, et al.
Published: (2024) -
A Solution toward Transparent and Practical AI Regulation: Privacy Nutrition Labels for Open-source Generative AI-based Applications
by: Si, Meixue, et al.
Published: (2024) -
A^3-CodGen: A Repository-Level Code Generation Framework for Code Reuse with Local-Aware, Global-Aware, and Third-Party-Library-Aware
by: Liao, Dianshu, et al.
Published: (2023) -
Deployability-Centric Infrastructure-as-Code Generation: Fail, Learn, Refine, and Succeed through LLM-Empowered DevOps Simulation
by: Zhang, Tianyi, et al.
Published: (2025) -
A Large-scale Investigation of Semantically Incompatible APIs behind Compatibility Issues in Android Apps
by: Pan, Shidong, et al.
Published: (2024)