15 citations · 26 across the 15 of their papers we have counts for
7 papers · 1 filter
Domain-constrained Synthesis of Inconsistent Key Aspects in Textual Vulnerability Descriptions
Linyi Han, Shidong Pan, Zhenchang Xing +5
Textual Vulnerability Descriptions (TVDs) are crucial for security analysts to understand and address software vulnerabilities. However, the key aspect inconsistencies in TVDs from…
LLM-as-a-Judge for Software Engineering: Literature Review, Vision, and the Road Ahead
Junda He, Jieke Shi, Terry Yue Zhuo +5
The rapid integration of Large Language Models (LLMs) into software engineering (SE) has revolutionized tasks like code generation, producing a massive volume of software artifacts…
HackWorld: Evaluating Computer-Use Agents on Exploiting Web Application Vulnerabilities
Xiaoxue Ren, Penghao Jiang, Kaixin Li +6
Web applications are prime targets for cyberattacks as gateways to critical services and sensitive data. Traditional penetration testing is costly and expertise-intensive, making i…
Bridging Solidity Evolution Gaps: An LLM-Enhanced Approach for Smart Contract Compilation Error Resolution
Likai Ye, Mengliang Li, Dehai Zhao +2
Solidity, the dominant smart contract language for Ethereum, has rapidly evolved with frequent version updates to enhance security, functionality, and developer experience. However…
From Code to Courtroom: LLMs as the New Software Judges
Junda He, Jieke Shi, Terry Yue Zhuo +5
Recently, Large Language Models (LLMs) have been increasingly used to automate SE tasks such as code generation and summarization. However, evaluating the quality of LLM-generated…
Identifying and Mitigating API Misuse in Large Language Models
Terry Yue Zhuo, Junda He, Jiamou Sun +4
API misuse in code generated by large language models (LLMs) presents a serious and growing challenge in software development, as although LLMs demonstrate impressive code generati…
50 Shades of Deceptive Patterns: A Unified Taxonomy, Multimodal Detection, and Security Implications
Zewei Shi, Ruoxi Sun, Jieshan Chen +5
Deceptive patterns (DPs) are user interface designs deliberately crafted to manipulate users into unintended decisions, often by exploiting cognitive biases for the benefit of comp…