8 papers
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…
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…
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…
Think Like Human Developers: Harnessing Community Knowledge for Structured Code Reasoning
Chengran Yang, Zhensu Sun, Hong Jin Kang +2
Large Language Models (LLMs) have significantly advanced automated code generation, yet they struggle with complex coding tasks requiring multi-step logical reasoning. High-quality…
Shelving it rather than Ditching it: Dynamically Debloating DEX and Native Methods of Android Applications without APK Modification
Zicheng Zhang, Jiakun Liu, Ferdian Thung +10
Today's Android developers tend to include numerous features to accommodate diverse user requirements, which inevitably leads to bloated apps. Yet more often than not, only a fract…