8 citations · 22 across the 13 of their papers we have counts for
13 papers
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…
From Exploration to Revelation: Detecting Dark Patterns in Mobile Apps
Jieshan Chen, Zhen Wang, Jiamou Sun +5
Mobile apps are essential in daily life but frequently employ deceptive patterns, such as visual emphasis or linguistic nudging, to manipulate user behavior. Existing research larg…
Let's Discover More API Relations: A Large Language Model-based AI Chain for Unsupervised API Relation Inference
Qing Huang, Yanbang Sun, Zhenchang Xing +5
APIs have intricate relations that can be described in text and represented as knowledge graphs to aid software engineering tasks. Existing relation extraction methods have limitat…
Let's Chat to Find the APIs: Connecting Human, LLM and Knowledge Graph through AI Chain
Qing Huang, Zhenyu Wan, Zhenchang Xing +4
API recommendation methods have evolved from literal and semantic keyword matching to query expansion and query clarification. The latest query clarification method is knowledge gr…
Adaptive Intellect Unleashed: The Feasibility of Knowledge Transfer in Large Language Models
Qing Huang, Yishun Wu, Zhenchang Xing +3
We conduct the first empirical study on using knowledge transfer to improve the generalization ability of large language models (LLMs) in software engineering tasks, which often re…
A Chain of AI-based Solutions for Resolving FQNs and Fixing Syntax Errors in Partial Code
Qing Huang, Jiahui Zhu, Zhenchang Xing +3
API documentation, technical blogs and programming Q&A sites contain numerous partial code that can be reused in programming tasks, but often these code are uncompilable due to unr…
Prompt Sapper: LLM-Empowered Software Engineering Infrastructure for AI-Native Services
Zhenchang Xing, Qing Huang, Yu Cheng +3
Foundation models, such as GPT-4, DALL-E have brought unprecedented AI "operating system" effect and new forms of human-AI interaction, sparking a wave of innovation in AI-native s…
Prompt Sapper: A LLM-Empowered Production Tool for Building AI Chains
Yu Cheng, Jieshan Chen, Qing Huang +3
The emergence of foundation models, such as large language models (LLMs) GPT-4 and text-to-image models DALL-E, has opened up numerous possibilities across various domains. People…
AI Chain on Large Language Model for Unsupervised Control Flow Graph Generation for Statically-Typed Partial Code
Qing Huang, Zhou Zou, Zhenchang Xing +3
Control Flow Graphs (CFGs) are essential for visualizing, understanding and analyzing program behavior. For statically-typed programming language like Java, developers obtain CFGs…
Answering Uncertain, Under-Specified API Queries Assisted by Knowledge-Aware Human-AI Dialogue
Qing Huang, Zishuai Li, Zhenchang Xing +4
Developers' API needs should be more pragmatic, such as seeking suggestive, explainable, and extensible APIs rather than the so-called best result. Existing API search research can…
API Entity and Relation Joint Extraction from Text via Dynamic Prompt-tuned Language Model
Qing Huang, Yanbang Sun, Zhenchang Xing +3
Extraction of Application Programming Interfaces (APIs) and their semantic relations from unstructured text (e.g., Stack Overflow) is a fundamental work for software engineering ta…
SE Factual Knowledge in Frozen Giant Code Model: A Study on FQN and its Retrieval
Qing Huang, Dianshu Liao, Zhenchang Xing +4
Pre-trained giant code models (PCMs) start coming into the developers' daily practices. Understanding what types of and how much software knowledge is packed into PCMs is the found…
Prompt-tuned Code Language Model as a Neural Knowledge Base for Type Inference in Statically-Typed Partial Code
Qing Huang, Zhiqiang Yuan, Zhenchang Xing +3
Partial code usually involves non-fully-qualified type names (non-FQNs) and undeclared receiving objects. Resolving the FQNs of these non-FQN types and undeclared receiving objects…