26 citations · 66 across the 11 of their papers we have counts for
10 papers · 1 filter
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…
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…
Automatically Removing Unnecessary Stubbings from Test Suites
Mengzhen Li, Mattia Fazzini
Most modern software systems are characterized by a high number of components whose interactions can affect and complicate testing activities. During testing, developers can accoun…
Automatically Detecting API-induced Compatibility Issues in Android Apps: A Comparative Analysis (Replicability Study)
Pei Liu, Yanjie Zhao, Haipeng Cai +3
Fragmentation is a serious problem in the Android ecosystem. This problem is mainly caused by the fast evolution of the system itself and the various customizations independently m…