评估基于任务的聊天机器人：用于Dialogflow的快照和精心整理的数据集
Assessing Task-based Chatbots: Snapshot and Curated Datasets for Dialogflow
AI总结：
本文提出TOFU-D和COD两个数据集，用于评估基于任务的聊天机器人质量和安全性的实证研究。
摘要：
近年来，由于聊天机器人能够随时帮助用户并在各种领域中发挥作用，它们得到了广泛应用。然而，缺乏大规模的精心整理数据集限制了对其质量和可靠性研究的深入。本文提出了TOFU-D，即从GitHub收集的1,788个Dialogflow聊天机器人快照，以及COD，即包含185个经过验证聊天机器人的精心整理子集。这两个数据集涵盖了广泛的主题、语言和实现模式，为聊天机器人质量和安全性的实证研究提供了坚实的基础。使用Botium测试框架和Bandit静态分析器进行的初步评估揭示了测试覆盖率的不足以及多个聊天机器人中频繁出现的安全漏洞，突显了对聊天机器人质量和安全性的系统、多平台研究的必要性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
