30 citations · 39 across the 12 of their papers we have counts for
13 papers
LoLBench: Evaluating Coding Agents with Long-Horizon Proposals on Large Software Systems
Yun Peng, Zihan Wu, Zeyang Zhuang +6
Modern coding agents can deliver increasingly large repository-level changes, and recent benchmarks reflect this by emphasizing long-horizon tasks with large reference implementati…
A Unified Issue Resolution Benchmark for Requirement Clarification, Planning, and Code Generation for Coding Agents
Xin Zhou, Chun Yong Chong, Kisub Kim +11
Large language model-powered coding agents are increasingly used to modify existing code repositories, for example, by adding features or fixing bugs. Yet existing repository-level…
What Resolve Rate Hides: Trajectory Structure Diagnostics for Coding Agents
Rui Shu, Chun Yong Chong, Xin Zhou +6
Coding agents are ranked almost entirely by resolve rate: whether their final patch passes the target tests. Yet two agents can reach the same outcome through very different proces…
SemiScope: Disentangling Classifier Tuning and Joint Optimization in Semi-Supervised Security Classification
Rui Shu, Tianpei Xia, Jingzhu He
Background. Labeled data for security classification is scarce. Semi-supervised learning (SSL) propagates labels from a small labeled pool to larger unlabeled pools. Yet security a…
Do I really need all this work to find vulnerabilities? An empirical case study comparing vulnerability detection techniques on a Java application
Sarah Elder, Nusrat Zahan, Rui Shu +4
CONTEXT: Applying vulnerability detection techniques is one of many tasks using the limited resources of a software project. OBJECTIVE: The goal of this research is to assist manag…
Reducing the Cost of Training Security Classifier (via Optimized Semi-Supervised Learning)
Rui Shu, Tianpei Xia, Huy Tu +2
Background: Most of the existing machine learning models for security tasks, such as spam detection, malware detection, or network intrusion detection, are built on supervised mach…
Dazzle: Using Optimized Generative Adversarial Networks to Address Security Data Class Imbalance Issue
Rui Shu, Tianpei Xia, Laurie Williams +1
Background: Machine learning techniques have been widely used and demonstrate promising performance in many software security tasks such as software vulnerability prediction. Howev…
Structuring a Comprehensive Software Security Course Around the OWASP Application Security Verification Standard
Sarah Elder, Nusrat Zahan, Val Kozarev +3
Lack of security expertise among software practitioners is a problem with many implications. First, there is a deficit of security professionals to meet current needs. Additionally…
Omni: Automated Ensemble with Unexpected Models against Adversarial Evasion Attack
Rui Shu, Tianpei Xia, Laurie Williams +1
Background: Machine learning-based security detection models have become prevalent in modern malware and intrusion detection systems. However, previous studies show that such model…
Predicting Health Indicators for Open Source Projects (using Hyperparameter Optimization)
Tianpei Xia, Wei Fu, Rui Shu +2
Software developed on public platform is a source of data that can be used to make predictions about those projects. While the individual developing activity may be random and hard…
Sequential Model Optimization for Software Process Control
Tianpei Xia, Rui Shu, Xipeng Shen +1
Many methods have been proposed to estimate how much effort is required to build and maintain software. Much of that research assumes a ``classic'' waterfall-based approach rather…
How to Better Distinguish Security Bug Reports (using Dual Hyperparameter Optimization
Rui Shu, Tianpei Xia, Jianfeng Chen +2
Background: In order that the general public is not vulnerable to hackers, security bug reports need to be handled by small groups of engineers before being widely discussed. But l…
Better Security Bug Report Classification via Hyperparameter Optimization
Rui Shu, Tianpei Xia, Laurie Williams +1
When security bugs are detected, they should be (a)~discussed privately by security software engineers; and (b)~not mentioned to the general public until security patches are avail…