10 citations · 50 across the 36 of their papers we have counts for
18 papers · 1 filter
SWE-Duel: Towards a Self-Scaling Arena for Coding Agents
Wei Minn, Zhensu Sun, Lwin Khin Shar +1
Static coding benchmarks are fixed at release. Over time their tasks leak into training data, and stronger agents eventually saturate them. We argue that a benchmark should instead…
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…
Trustworthy Runtime Error Healing in Real-World Repositories: A Benchmark and Guardrail
Gou Tan, Pengfei Chen, Zhensu Sun +9
Runtime error healing lets a crashed program continue by generating code that repairs its live runtime state. Recent work shows that LLMs can generate such healing code, but it is…
VulContextBench: A Benchmark for Security Context Retrieval in Coding Agents
Yikun Li, Jinfeng Jiang, Yuheng Yieh +6
Vulnerability-detection benchmarks score the verdict an agent reaches, not the evidence it gathered. A model that recalls a CVE from pretraining therefore scores the same as one th…
CoSA: Context-Aware Severity Assessment via Context Analysis with Large Language Models
Jinfeng Jiang, Yikun Li, Chengran Yang +6
Accurate vulnerability severity assessment is essential for prioritizing remediation, yet manually assessing Common Vulnerability Scoring System (CVSS) base metrics remains labor-i…
AgentChaos: Chaos Engineering for Agent Systems via Programmatic Fault Injection
Gou Tan, Zhensu Sun, Jieke Shi +10
Agent systems rely on LLM APIs for every response, but these APIs can return server errors, truncated responses, or corrupted content that propagates through downstream agents and…
Graph Is the Verifier: Agentic Reinforcement Learning for Interprocedural Vulnerability Detection
Yikun Li, Ting Zhang, Jiakun Liu +9
Real-world vulnerabilities often span multiple functions, yet most learning-based detectors classify each function in isolation: on a sample of real CVEs, we find that 71.7% of vul…
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…
Beyond the Tip of the Iceberg: Understanding SATD in Dockerfiles through the Lens of Co-evolution
Wei Minn, Yan Naing Tun, Biniam Fesseha Demissie +5
Dockerfiles enable the creation of portable container-based execution environments for the application code, and have become an important part of the modern software development pr…
An Execution-Verified Multi-Language Benchmark for Code Semantic Reasoning
Yikun Li, Jinfeng Jiang, Ting Zhang +7
Evaluating whether large language models (LLMs) can recover execution-relevant program structure, rather than only produce code that passes tests, remains an open problem. Existing…
From Incomplete Architecture to Quantified Risk: Multimodal LLM-Driven Security Assessment for Cyber-Physical Systems
Shaofei Huang, Christopher M. Poskitt, Lwin Khin Shar
Cyber-physical systems often contend with incomplete architectural documentation or outdated information resulting from legacy technologies, knowledge management gaps, and the comp…
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…
Beyond Function-Level Analysis: Context-Aware Reasoning for Inter-Procedural Vulnerability Detection
Yikun Li, Ting Zhang, Jieke Shi +10
Recent progress in ML and LLMs has improved vulnerability detection, and recent datasets have reduced label noise and unrelated code changes. However, most existing approaches stil…
Virtualization-based Penetration Testing Study for Detecting Accessibility Abuse Vulnerabilities in Banking Apps in East and Southeast Asia
Wei Minn, Phong Phan, Vikas K. Malviya +6
Android banking applications have revolutionized financial management by allowing users to perform various financial activities through mobile devices. However, this convenience ha…
CovAgent: Overcoming the 30% Curse of Mobile Application Coverage with Agentic AI and Dynamic Instrumentation
Wei Minn, Biniam Fisseha Demissie, Yan Naing Tun +4
Automated GUI testing is crucial for ensuring the quality and reliability of Android apps. However, the efficacy of existing UI testing techniques is often limited, especially in t…
PenForge: On-the-Fly Expert Agent Construction for Automated Penetration Testing
Huihui Huang, Jieke Shi, Junkai Chen +6
Penetration testing is essential for identifying vulnerabilities in web applications before real adversaries can exploit them. Recent work has explored automating this process with…
LIDL: LLM Integration Defect Localization via Knowledge Graph-Enhanced Multi-Agent Analysis
Gou Tan, Zilong He, Min Li +8
LLM-integrated software, which embeds or interacts with large language models (LLMs) as functional components, exhibits probabilistic and context-dependent behaviors that fundament…