135 citations · 192 across the 26 of their papers we have counts for
31 papers
MetaVul: Agentic Harness Discovery for Vulnerability Detection
Ira Ceka, Arnav Garg, Ryan Lutz +4
LLM agents are increasingly used to automate vulnerability detection, but reliable detection is challenging because agents must distinguish subtle, security-relevant fixes from ben…
ExploreRepair: Evidence-Specialized Agents for Vulnerability Repair under Information Constraints
Duc Manh Tran, Ratnadira Widyasari, Ivana Clairine Irsan +5
Automated vulnerability repair must often operate with incomplete vulnerability information: developers may lack an accurate vulnerability classification or a provided proof-of-vul…
Engineering Sustainable Agents: A Systematic Comparison of Agentic LLMs for Developer Workflows
Merve Astekin, Yan Naing Tun, Arda Goknil +5
Large language models (LLMs) are increasingly used in software engineering, including agentic systems that coordinate multiple agents, but impose higher computational and environme…
Towards Scalable and Cost-Efficient Vulnerability Detection: A Study on Automatic Query Generation
Ivana Clairine Irsan, Ratnadira Widyasari, Huihui Huang +6
Static analysis remains a cornerstone of software security, yet the effectiveness of tools such as CodeQL is often limited by the substantial manual effort required to develop high…
We Must Have Missed This Comment: Detecting and Repairing Stale Function References in Linux Kernel Comments
Kexin Sun, Yunbo Lyu, Xutong Ma +6
As the Linux kernel evolves, code comments may become outdated, as the functions they reference can be refactored or removed independently without corresponding updates to the comm…
"Go Home Copilot, You're Drunk": Understanding Developer Responses to Agent-Generated Code Review Comments
Shamse Tasnim Cynthia, Ratnadira Widyasari, Banani Roy +2
Code review is a critical quality assurance practice in software engineering development, and AI coding agents are increasingly generating review comments on pull requests. However…
PoCEvolve: Generating Proof-of-Concept Exploits from Security Patches with Vulnerability-Aware Prompt Evolution
Duc Manh Tran, Ratnadira Widyasari, Ivana Clairine Irsan +6
Ideally, the detailed information about a vulnerability should be made available together with the fixing commit. In practice, however, such details often become available only lon…
From Generic to Personalized: Exploring Persona-Aware Code Review Explanations
Shamse Tasnim Cynthia, Ratnadira Widyasari, Banani Roy +2
Code review is essential for ensuring software quality and supporting collaboration, yet prior work shows that developers can interpret code review comments differently. These diff…
Debt Behind the AI Boom: A Large-Scale Empirical Study of AI-Generated Code in the Wild
Yue Liu, Ratnadira Widyasari, Yanjie Zhao +3
AI coding assistants are now widely used in software development. Software developers increasingly integrate AI-generated code into their codebases to improve productivity. Prior s…
AgentSZZ: Teaching the LLM Agent to Play Detective with Bug-Inducing Commits
Yunbo Lyu, Jieke Shi, Hong Jin Kang +8
The SZZ algorithm is the dominant technique for identifying bug-inducing commits and underpins many software engineering tasks, such as defect prediction and vulnerability analysis…
TitanCA: Lessons from Orchestrating LLM Agents to Discover 100+ CVEs
Ting Zhang, Yikun Li, Chengran Yang +15
Software vulnerabilities remain one of the most persistent threats to modern digital infrastructure. While static application security testing (SAST) tools have long served as the…
Revisiting Vulnerability Patch Identification on Data in the Wild
Ivana Clairine Irsan, Ratnadira Widyasari, Ting Zhang +7
Attacks can exploit zero-day or one-day vulnerabilities that are not publicly disclosed. To detect these vulnerabilities, security researchers monitor development activities in ope…
Back to the Basics: Rethinking Issue-Commit Linking with LLM-Assisted Retrieval
Huihui Huang, Ratnadira Widyasari, Ting Zhang +8
Issue-commit linking, which connects issues with commits that fix them, is crucial for software maintenance. Existing approaches have shown promise in automatically recovering thes…
Mapping NVD Records to Their Vulnerability-fixing Commits: How Hard is It?
Huu Hung Nguyen, Ting Zhang, Duc Manh Tran +7
Mapping National Vulnerability Database (NVD) records to vulnerability-fixing commits (VFCs) is crucial for vulnerability analysis but challenging due to sparse explicit links in N…
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…
LessLeak-Bench: A First Investigation of Data Leakage in LLMs Across 83 Software Engineering Benchmarks
Xin Zhou, Martin Weyssow, Ratnadira Widyasari +7
Large Language Models (LLMs) are widely utilized in software engineering (SE) tasks, such as code generation and automated program repair. However, their reliance on extensive and…
CleanVul: Automatic Function-Level Vulnerability Detection in Code Commits Using LLM Heuristics
Yikun Li, Ting Zhang, Ratnadira Widyasari +13
Accurate identification of software vulnerabilities is crucial for system integrity. Vulnerability datasets, often derived from the National Vulnerability Database (NVD) or directl…
Beyond ChatGPT: Enhancing Software Quality Assurance Tasks with Diverse LLMs and Validation Techniques
Ratnadira Widyasari, David Lo, Lizi Liao
With the advancement of Large Language Models (LLMs), their application in Software Quality Assurance (SQA) has increased. However, the current focus of these applications is predo…
BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions
Terry Yue Zhuo, Minh Chien Vu, Jenny Chim +30
Task automation has been greatly empowered by the recent advances in Large Language Models (LLMs) via Python code, where the tasks ranging from software engineering development to…
Demystifying Faulty Code with LLM: Step-by-Step Reasoning for Explainable Fault Localization
Ratnadira Widyasari, Jia Wei Ang, Truong Giang Nguyen +2
Fault localization is a critical process that involves identifying specific program elements responsible for program failures. Manually pinpointing these elements, such as classes,…
BugsInPy: A Database of Existing Bugs in Python Programs to Enable Controlled Testing and Debugging Studies
Ratnadira Widyasari, Sheng Qin Sim, Camellia Lok +13
The 2019 edition of Stack Overflow developer survey highlights that, for the first time, Python outperformed Java in terms of popularity. The gap between Python and Java further wi…
A Systematic Literature Review on Explainability for Machine/Deep Learning-based Software Engineering Research
Sicong Cao, Xiaobing Sun, Ratnadira Widyasari +8
The remarkable achievements of Artificial Intelligence (AI) algorithms, particularly in Machine Learning (ML) and Deep Learning (DL), have fueled their extensive deployment across…
Explaining Explanation: An Empirical Study on Explanation in Code Reviews
Ratnadira Widyasari, Ting Zhang, Abir Bouraffa +2
Code reviews are central for software quality assurance. Ideally, reviewers should explain their feedback to enable authors of code changes to understand the feedback and act accor…