# MutaBot: A Mutation Testing Approach for Chatbots

arXiv: https://arxiv.org/abs/2401.10372 · HTML: https://mlsanity.com/paper/2401.10372/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 30 references that are in the map.
- First posted on arXiv 18 Jan 2024.

## Builds on (most cited first, 2 of 2)

- [Survey on Evaluation Methods for Dialogue Systems](https://mlsanity.com/paper/1905.04071/) (2019)
- [Imperceptible, Robust, and Targeted Adversarial Examples for Automatic Speech Recognition](https://mlsanity.com/paper/1903.10346/) (2019)

## Abstract (arXiv)

Mutation testing is a technique aimed at assessing the effectiveness of test suites by seeding artificial faults into programs. Although available for many platforms and languages, no mutation testing tool is currently available for conversational chatbots, which represent an increasingly popular solution to design systems that can interact with users through a natural language interface. Note that since conversations must be explicitly engineered by the developers of conversational chatbots, these systems are exposed to specific types of faults not supported by existing mutation testing tools. In this paper, we present MutaBot, a mutation testing tool for conversational chatbots. MutaBot addresses mutations at multiple levels, including conversational flows, intents, and contexts. We designed the tool to potentially target multiple platforms, while we implemented initial support for Google Dialogflow chatbots. We assessed the tool with three Dialogflow chatbots and test cases generated with Botium, revealing weaknesses in the test suites.