Improving LLM-Driven Test Generation by Learning from Mocking Information (AIST 2026) — Replication Package

Authors:
Jamie Lee*, Flynn Teh*, Hengcheng Zhu†, Mengzhen Li‡, Mattia Fazzini‡, Valerio Terragni*

Affiliations:

* University of Auckland, Auckland, New Zealand
† The Hong Kong University of Science and Technology, Hong Kong SAR
‡ University of Minnesota, Minneapolis, USA

This repository is the replication package for the paper “Improving LLM-Driven Test Generation by Learning from Mocking Information,” presented at AIST 2026. It contains the MOCKMILL tool implementation, raw experimental results, subject programs, and analysis scripts required to reproduce the study. MOCKMILL is a Java-based approach for unit test generation with large language models that leverages mocking information to improve test quality and coverage. The package includes materials for tool setup, experiment execution, and result analysis, and is intended to support transparency, reproducibility, and follow-up research in AI-based software testing.

Citation (BibTeX)

@inproceedings{lee2026mockmill,
title={Improving LLM-Driven Test Generation by Learning from Mocking Information},
author={Lee, Jamie and Teh, Flynn and Zhu, Hengcheng and Li, Mengzhen and Fazzini, Mattia and Terragni, Valerio},
booktitle={Proceedings of the 19th International Conference on Software Testing, Verification and Validation Workshops (AIST)},
year={2026},
organization={IEEE}
}