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20232026
most citedUsing Cooperative Co-evolutionary Search to Generate Metamorphic Test Cases for Autonomous Driving Systems

6 citations · 10 across the 13 of their papers we have counts for

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14 papers · 1 filter

cs.SE2026

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…

cs.SE2026

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…

cs.SE2026

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…

cs.SE2026

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),…

cs.SE2026

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…

cs.SE2026

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…

cs.SE2026

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…

cs.SE2026

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…

cs.SE2026

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…

cs.SE2025

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…

cs.SE2025

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…

cs.SE2025★ 2 cited

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…

cs.SE2024★ 6 cited

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…

cs.SE2023★ 2 cited

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…