6 citations · 10 across the 13 of their papers we have counts for
17 papers
SpecGuardBench: Can Coding Agents Construct Reliable Validation Harnesses from Natural Language Specifications?
Zhiqing Zhong, Mingyu Chen, Aoyang Fang +5
As coding agents become more capable, tests remain the primary evidence for judging whether their code is correct. Yet passing available tests does not establish that an implementa…
AdaT: Adaptive Test Transformations for Black-Box Boundary Testing of Conversational Agents
Liting Lin, Boxi Yu, Qinghua Xu +3
Conversational agents based on large language models (LLMs) must comply with policies. Each condition in a policy draws a boundary between user requests, and the agent must behave…
When Is Graph Structure Worth Its Cost? The Case for Structure Pricing in Retrieval-Augmented Generation
Yuzhong Zhang, Haoyang Ma, Chao Peng +3
Graph-based retrieval-augmented generation (RAG) can help answer questions that require information from many documents. However, building a graph often requires many language-mode…
Mining Workflow Graphs for Black-Box Boundary Testing of Conversational LLM Agents
Liting Lin, Boxi Yu, Yuzhong Zhang +3
Conversational LLM agents can cause real-world harm when their internal workflows fail, such as completing a transaction without confirmation. Testing these state-dependent failure…
EvoEye: Self-Evolving Runtime Monitoring for Autonomous Driving Systems
Mingfei Cheng, Lionel Briand, Xiaofei Xie
Runtime monitoring is essential for detecting impending hazards in autonomous driving systems (ADSs). However, existing ADS runtime monitors have fixed detection capabilities: rule…
Supporting System Testing with a Multi-Agent LLM-based Framework for Knowledge Graph Extraction: A Case Study with Ethernet Switch Systems
Rongqi Pan, Mahboubeh Dadkhah, Jean Baptiste Minani +3
Technical documents contain rich domain knowledge for automating downstream tasks such as system testing. While this paper focuses on Ethernet switch configuration manuals (ESCMs),…
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…
Similar Pattern Annotation via Retrieval Knowledge for LLM-Based Test Code Fault Localization
Golnaz Gharachorlu, Mahsa Panahandeh, Lionel C. Briand +2
Software failures remain a major challenge in modern software development, and identifying the code elements responsible for failures is a time-consuming debugging task. While exte…
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…
Retromorphic Testing with Hierarchical Verification for Hallucination Detection in RAG
Boxi Yu, Yuzhong Zhang, Liting Lin +2
Large language models can still hallucinate in retrieval-augmented generation (RAG), producing claims that are unsupported by or conflict with the retrieved context. Detecting such…
Automating the Detection of Requirement Dependencies Using Large Language Models
Ikram Darif, Feifei Niu, Manel Abdellatif +3
Requirements are inherently interconnected through various types of dependencies. Identifying these dependencies is essential, as they underpin critical decisions and influence a r…
LLM-Driven Cost-Effective Requirements Change Impact Analysis
Romina Etezadi, Sallam Abualhaija, Chetan Arora +1
Requirements are inherently subject to change throughout the software development lifecycle. Within the limited budget available to requirements engineers, manually identifying the…
Constrained Co-evolutionary Metamorphic Differential Testing for Autonomous Systems with an Interpretability Approach
Hossein Yousefizadeh, Shenghui Gu, Lionel C. Briand +1
Autonomous systems, such as autonomous driving systems, evolve rapidly through frequent updates, risking unintended behavioral degradations. Effective system-level testing is chall…
Classifier or Prompt: A Case Study on Legal Requirements Traceability
Romina Etezadi, Sallam Abualhaija, Chetan Arora +1
New regulations are introduced to ensure software development aligns with ethical concerns and protects public safety. Showing compliance requires tracing requirements to legal pro…
Using Cooperative Co-evolutionary Search to Generate Metamorphic Test Cases for Autonomous Driving Systems
Hossein Yousefizadeh, Shenghui Gu, Lionel C. Briand +1
Autonomous Driving Systems (ADSs) rely on Deep Neural Networks, allowing vehicles to navigate complex, open environments. However, the unpredictability of these scenarios highlight…
Legal Requirements Analysis
Sallam Abualhaija, Marcello Ceci, Lionel Briand
Modern software has been an integral part of everyday activities in many disciplines and application contexts. Introducing intelligent automation by leveraging artificial intellige…