An Automated Testing Framework for Conversational Agents
This work addresses the need for better testing tools for conversational agent developers, but it is incremental as it builds on existing testing concepts.
The paper tackles the limited automated testing infrastructure for conversational agents by introducing a work-in-progress framework in Python, addressing research problems like test oracles and semantic utterance comparison to improve system quality.
Conversational agents are systems with a conversational interface that afford interaction in spoken language. These systems are becoming prevalent and are preferred in various contexts and for many users. Despite their increasing success, the automated testing infrastructure to support the effective and efficient development of such systems compared to traditional software systems is still limited. Automated testing framework for conversational systems can improve the quality of these systems by assisting developers to write, execute, and maintain test cases. In this paper, we introduce our work-in-progress automated testing framework, and its realization in the Python programming language. We discuss some research problems in the development of such an automated testing framework for conversational agents. In particular, we point out the problems of the specification of the expected behavior, known as test oracles, and semantic comparison of utterances.