6 papers
CUDABeaver: Benchmarking LLM-Based Automated CUDA Debugging
Shiyang Li, Haoyang Chen, Mattia Fazzini +1
Debugging CUDA programs has long been challenging because failures often arise from subtle interactions among hardware behavior, compiler decisions, memory hierarchy, and asynchron…
Improving LLM-Driven Test Generation by Learning from Mocking Information
Jamie Lee, Flynn Teh, Hengcheng Zhu +3
Large Language Models (LLMs) have recently shown strong potential for automated unit test generation. This has motivated us to investigate whether developer-defined test doubles (c…
Human-Agent versus Human Pull Requests: A Testing-Focused Characterization and Comparison
Roberto Milanese, Francesco Salzano, Angelica Spina +4
AI-based coding agents are increasingly integrated into software development workflows, collaborating with developers to create pull requests (PRs). Despite their growing adoption,…
An Exploratory Study of Bayesian Prompt Optimization for Test-Driven Code Generation with Large Language Models
Shlok Tomar, Aryan Deshwal, Ethan Villalovoz +3
We consider the task of generating functionally correct code using large language models (LLMs). The correctness of generated code is influenced by the prompt used to query the giv…
LADs: Leveraging LLMs for AI-Driven DevOps
Ahmad Faraz Khan, Azal Ahmad Khan, Anas Mohamed +7
Automating cloud configuration and deployment remains a critical challenge due to evolving infrastructures, heterogeneous hardware, and fluctuating workloads. Existing solutions la…
ICST Tool Competition 2025 -- Self-Driving Car Testing Track
Christian Birchler, Stefan Klikovits, Mattia Fazzini +1
This is the first edition of the tool competition on testing self-driving cars (SDCs) at the International Conference on Software Testing, Verification and Validation (ICST). The a…