9 papers
TATG: Tracking-Aware Testing Objective for LLM-based Test Generation
Guancheng Wang, Qinghua Xu, Lionel C. Briand
Complex Java methods remain challenging for automated unit test generation because achieving high coverage and fault detection often requires satisfying branch-specific testing req…
BeSpec: Behavior-Level Specification Alignment for Code Generation
Qinghua Xu, Guancheng Wang, Boxi Yu +1
LLMs have made substantial progress on automated code generation from natural-language descriptions of desired behavior (intent). Most existing methods improve generated programs t…
Uncertainty-Guided Label Rebalancing for CPS Safety Monitoring
John Ayotunde, Qinghua Xu, Guancheng Wang +1
Safety monitoring is essential for Cyber-Physical Systems (CPSs). However, unsafe events are rare in real-world CPS operations, creating an extreme class imbalance that degrades sa…
LLM-based Mockless Unit Test Generation for Java
Qinghua Xu, Guancheng Wang, Lionel Briand +2
Large language models (LLMs) have shown strong potential for automated test generation, yet most approaches to generating Java unit tests still rely on mocking frameworks to handle…
Call-Chain-Aware LLM-Based Test Generation for Java Projects
Guancheng Wang, Qinghua Xu, Lionel C. Briand +2
Large language models (LLMs) have recently shown strong potential for generating project-level unit tests. However, existing state-of-the-art approaches primarily rely on execution…
Mutation-Guided Unit Test Generation with a Large Language Model
Guancheng Wang, Qinghua Xu, Lionel Briand +1
Unit tests play a vital role in uncovering potential faults in software. While tools like EvoSuite focus on maximizing code coverage, recent advances in large language models (LLMs…