Assessing Task-based Chatbots: Snapshot and Curated Datasets for Dialogflow
Abstract
In recent years, chatbots have gained widespread adoption thanks to their ability to assist users at any time and across diverse domains. However, the lack of large-scale curated datasets limits research on their quality and reliability. This paper presents TOFU-D, a snapshot of 1,788 Dialogflow chatbots from GitHub, and COD, a curated subset of TOFU-D including 185 validated chatbots. The two datasets capture a wide range of domains, languages, and implementation patterns, offering a sound basis for empirical studies on chatbot quality and security. A preliminary assessment using the Botium testing framework and the Bandit static analyzer revealed gaps in test coverage and frequent security vulnerabilities in several chatbots, highlighting the need for systematic, multi-Platform research on chatbot quality and security.
Keywords
Cite
@article{arxiv.2601.19787,
title = {Assessing Task-based Chatbots: Snapshot and Curated Datasets for Dialogflow},
author = {Elena Masserini and Diego Clerissi and Daniela Micucci and Leonardo Mariani},
journal= {arXiv preprint arXiv:2601.19787},
year = {2026}
}
Comments
4 pages, 5 figures, Accepted at International Conference on Mining Software Repositories (MSR) 2026