MutaBot: A Mutation Testing Approach for Chatbots
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866914646012723200 |
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| author | Urrico, Michael Ferdinando Clerissi, Diego Mariani, Leonardo |
| author_facet | Urrico, Michael Ferdinando Clerissi, Diego Mariani, Leonardo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_10372 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MutaBot: A Mutation Testing Approach for Chatbots Urrico, Michael Ferdinando Clerissi, Diego Mariani, Leonardo Software Engineering Artificial Intelligence 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. |
| title | MutaBot: A Mutation Testing Approach for Chatbots |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2401.10372 |