1 citations · 1 across the 8 of their papers we have counts for
8 papers
LLM-Assisted Repository-Level Generation with Structured Spec-Driven Engineering
Shuzhao Feng, Boqi Chen, Brett H Meyer +1
State-of-the-art Large Language Models (LLMs) excel in code generation at the function level. However, the output quality significantly declines when scaling to repository-level sy…
Structure- and Event-Driven Frameworks for State Machine Modeling with Large Language Models
Samer Abdulkarim, Evan Boyd, Karl Bridi +3
UML state machine design is a critical process in software engineering. Traditionally, state machines are manually crafted by experienced engineers based on natural language requir…
SHERPA: A Model-Driven Framework for Large Language Model Execution
Boqi Chen, Kua Chen, José Antonio Hernández López +3
Recently, large language models (LLMs) have achieved widespread application across various fields. Despite their impressive capabilities, LLMs suffer from a lack of structured reas…
Accurate and Consistent Graph Model Generation from Text with Large Language Models
Boqi Chen, Ou Wei, Bingzhou Zheng +1
Graph model generation from natural language description is an important task with many applications in software engineering. With the rise of large language models (LLMs), there i…
LLM-based Satisfiability Checking of String Requirements by Consistent Data and Checker Generation
Boqi Chen, Aren A. Babikian, Shuzhao Feng +2
Requirements over strings, commonly represented using natural language (NL), are particularly relevant for software systems due to their heavy reliance on string data manipulation.…
The Power of Types: Exploring the Impact of Type Checking on Neural Bug Detection in Dynamically Typed Languages
Boqi Chen, José Antonio Hernández López, Gunter Mussbacher +1
Motivation: Automated bug detection in dynamically typed languages such as Python is essential for maintaining code quality. The lack of mandatory type annotations in such language…
Automated and Complete Generation of Traffic Scenarios at Road Junctions Using a Multi-level Danger Definition
Aren A. Babikian, Attila Ficsor, Oszkár Semeráth +2
To ensure their safe use, autonomous vehicles (AVs) must meet rigorous certification criteria that involve executing maneuvers safely within (arbitrary) scenarios where other actor…
Certifying Robustness of Graph Convolutional Networks for Node Perturbation with Polyhedra Abstract Interpretation
Boqi Chen, Kristóf Marussy, Oszkár Semeráth +2
Graph convolutional neural networks (GCNs) are powerful tools for learning graph-based knowledge representations from training data. However, they are vulnerable to small perturbat…