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DreamTest: World-Model Surrogates for Search-Based Testing of Deep Reinforcement Learning Agents
Qinghua Xu, Guancheng Wang, Boxi Yu +2
Testing deep reinforcement learning (DRL) agents in cyber-physical systems aims to uncover diverse failures before deployment, but each execution can be expensive. Surrogate-assist…
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…
Characterizing the Failure Modes of LLMs in Resolving Real-World GitHub Issues
Yanjie Jiang, Yian Huang, Guancheng Wang +3
Large Language Models (LLMs) are increasingly deployed to resolve real-world GitHub issues. However, despite their potential, the specific failure modes of these models in complex…
LLM-based Low-Level Integration Test Generation for Java
Qinghua Xu, Guancheng Wang, Lionel Briand +2
Large language models (LLMs) show promise for automated test generation, but most approaches target unit tests with mocked dependencies. Low-level integration testing instead exerc…
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…
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…
SWE-ABS: Adversarial Benchmark Strengthening Exposes Inflated Success Rates on Test-based Benchmark
Boxi Yu, Yang Cao, Yuzhong Zhang +9
The SWE-Bench Verified leaderboard is approaching saturation, with the top system achieving 78.80%. However, we show that this performance is inflated. Our re-evaluation reveals th…