3 citations · 6 across the 5 of their papers we have counts for
12 papers
Balancing Usefulness and Naturalness: An LLM-based Curation Pipeline for Code Review Comments
Oussama Ben Sghaier, Martin Weyssow, Houari Sahraoui
Code review is a cornerstone of software development, where reviewers provide feedback through written comments to ensure code quality, maintainability, and correctness. The effect…
Out of Distribution, Out of Luck: How Well Can LLMs Trained on Vulnerability Datasets Detect Top 25 CWE Weaknesses?
Yikun Li, Ngoc Tan Bui, Ting Zhang +16
Automated vulnerability detection research has made substantial progress, yet its real-world impact remains limited. Prior work found that current vulnerability datasets suffer fro…
An LLM-as-Judge Metric for Bridging the Gap with Human Evaluation in SE Tasks
Xin Zhou, Kisub Kim, Ting Zhang +6
Large Language Models (LLMs) and other automated techniques have been increasingly used to support software developers by generating software artifacts such as code snippets, patch…
Let the Trial Begin: A Mock-Court Approach to Vulnerability Detection using LLM-Based Agents
Ratnadira Widyasari, Martin Weyssow, Ivana Clairine Irsan +6
Detecting vulnerabilities in source code remains a critical yet challenging task, especially when benign and vulnerable functions share significant similarities. In this work, we i…
R2Vul: Learning to Reason about Software Vulnerabilities with Reinforcement Learning and Structured Reasoning Distillation
Martin Weyssow, Chengran Yang, Junkai Chen +12
Large language models (LLMs) have shown promising performance in software vulnerability detection, yet their reasoning capabilities remain unreliable. We propose R2Vul, a method th…
Benchmarking Large Language Models for Multi-Language Software Vulnerability Detection
Ting Zhang, Chengran Yang, Yindu Su +8
Recent advancements in generative AI have led to the widespread adoption of large language models (LLMs) in software engineering, addressing numerous long-standing challenges. Howe…