6 papers
Continuous Autonomous Refactoring: A Research Roadmap for AI-Driven Code Quality Maintenance
Xin Sun, Daniel Ståhl, Kristian Sandahl +1
Large language models have shown promising capabilities in code refactoring, but existing approaches remain limited to method-level tasks. In this paper, we envision LLM-based refa…
Do Newer Models Produce Better Patches? A Non-Functional Quality Study
Xin Sun, Daniel Ståhl, Kristian Sandahl +1
Repository-level coding benchmarks typically measure progress in model capability by comparing the resolved rates of later and earlier models. However, this focus overlooks whether…
Patch Validation in Automated Vulnerability Repair
Zheng Yu, Wenxuan Shi, Xinqian Sun +3
Automated Vulnerability Repair (AVR) systems, especially those leveraging large language models (LLMs), have demonstrated promising results in patching vulnerabilities -- that is,…
Seeing the Reasoning: How LLM Rationales Influence User Trust and Decision-Making in Factual Verification Tasks
Xin Sun, Shu Wei, Jos A Bosch +3
Large Language Models (LLMs) increasingly show reasoning rationales alongside their answers, turning "reasoning" into a user-interface element. While step-by-step rationales are ty…
BandFuzz: An ML-powered Collaborative Fuzzing Framework
Wenxuan Shi, Hongwei Li, Jiahao Yu +3
Collaborative fuzzing combines multiple individual fuzzers and dynamically chooses appropriate combinations for different programs. Unlike individual fuzzers that rely on specific…
A Large Language Model Approach to Identify Flakiness in C++ Projects
Xin Sun, Daniel Ståhl, Kristian Sandahl
The role of regression testing in software testing is crucial as it ensures that any new modifications do not disrupt the existing functionality and behaviour of the software syste…