Using Voice and Biofeedback to Predict User Engagement during Requirements Interviews

• URL: http://arxiv.org/abs/2104.02410v1
• Date: Tue, 6 Apr 2021 10:34:36 GMT
• Title: Using Voice and Biofeedback to Predict User Engagement during Requirements Interviews
• Authors: Alessio Ferrari, Thaide Huichapa, Paola Spoletini, Nicole Novielli, Davide Fucci, Daniela Girardi
• Abstract summary: We propose to utilize biometric data, in terms of physiological and voice features, to complement interviews with information about user engagement. We evaluate our approach by interviewing users while gathering their physiological data using an Empatica E4 wristband. Our results show that we can predict users' engagement by training supervised machine learning algorithms on biometric data.
• Score: 11.277063517143565
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Capturing users engagement is crucial for gathering feedback about the features of a software product. In a market-driven context, current approaches to collect and analyze users feedback are based on techniques leveraging information extracted from product reviews and social media. These approaches are hardly applicable in bespoke software development, or in contexts in which one needs to gather information from specific users. In such cases, companies need to resort to face-to-face interviews to get feedback on their products. In this paper, we propose to utilize biometric data, in terms of physiological and voice features, to complement interviews with information about the engagement of the user on the discussed product-relevant topics. We evaluate our approach by interviewing users while gathering their physiological data (i.e., biofeedback) using an Empatica E4 wristband, and capturing their voice through the default audio-recorder of a common laptop. Our results show that we can predict users' engagement by training supervised machine learning algorithms on biometric data, and that voice features alone can be sufficiently effective. The performance of the prediction algorithms is maximised when pre-processing the training data with the synthetic minority oversampling technique (SMOTE). The results of our work suggest that biofeedback and voice analysis can be used to facilitate prioritization of requirements oriented to product improvement, and to steer the interview based on users' engagement. Furthermore, the usage of voice features can be particularly helpful for emotion-aware requirements elicitation in remote communication, either performed by human analysts or voice-based chatbots.

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