3 citations · 4 across the 8 of their papers we have counts for
10 papers · 1 filter
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…
CASPER-Change-Aware Slice Prioritization for Efficient Regression Testing of LLM-based systems
Biruk Asmare Muse, Lionel Briand, Yiwei Lu +1
Regression testing for LLM-based systems poses unique challenges because individual regression instances provide limited information about system-level regressions. A failure in a…
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…
Reclaiming Software Engineering as the Enabling Technology for the Digital Age
Tanja E. J. Vos, Tijs van der Storm, Alexander Serebrenik +4
Software engineering is the invisible infrastructure of the digital age. Every breakthrough in artificial intelligence, quantum computing, photonics, and cybersecurity relies on ad…
Drivora: A Unified and Extensible Infrastructure for Search-based Autonomous Driving Testing
Mingfei Cheng, Lionel Briand, Yuan Zhou
Search-based testing is critical for evaluating the safety and reliability of autonomous driving systems (ADSs). However, existing approaches are often built on heterogeneous frame…
Beyond Strict Rules: Assessing the Effectiveness of Large Language Models for Code Smell Detection
Saymon Souza, Amanda Santana, Eduardo Figueiredo +3
Code smells are symptoms of potential code quality problems that may affect software maintainability, thus increasing development costs and impacting software reliability. Large la…
Efficient Black-Box Fault Localization for System-Level Test Code Using Large Language Models
Ahmadreza Saboor Yaraghi, Golnaz Gharachorlu, Sakina Fatima +3
Fault localization (FL) is a critical step in debugging, which typically relies on repeated executions to pinpoint faulty code regions. However, repeated executions can be impracti…
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…
Hallucination to Consensus: Multi-Agent LLMs for End-to-End JUnit Test Generation
Qinghua Xu, Guancheng Wang, Lionel Briand +1
Unit testing plays a critical role in ensuring software correctness. However, writing unit tests manually is labor-intensive, especially for strongly typed languages like Java, mot…