{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,4,18]],"date-time":"2024-04-18T02:17:22Z","timestamp":1713406642198},"reference-count":140,"publisher":"Association for Computing Machinery (ACM)","issue":"4","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Softw. Eng. Methodol."],"published-print":{"date-parts":[[2024,5,31]]},"abstract":"\n Capturing users\u2019 engagement is crucial for gathering feedback about the features of a software product. In a market-driven context, current approaches to collecting and analyzing users\u2019 feedback are based on techniques leveraging information extracted from product reviews and social media. These approaches are hardly applicable in contexts where online feedback is limited, as for the majority of apps, and software in general. In such cases, companies need to resort to face-to-face interviews to get feedback on their products. In this article, we propose to utilize biometric data, in terms of physiological and voice features, to complement product feedback interviews with information about the engagement of the user on product-relevant topics. We evaluate our approach by interviewing users while gathering their physiological data (i.e.,\n biofeedback<\/jats:italic>\n ) 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\u2019 engagement by training supervised machine learning algorithms on biofeedback and voice data, and that voice features alone can be sufficiently effective. The best configurations evaluated achieve an average F1 \u223c 70% in terms of classification performance, and use voice features only. This work is one of the first studies in requirements engineering in which biometrics are used to identify emotions. Furthermore, this is one of the first studies in software engineering that considers voice analysis. The usage of voice features can be particularly helpful for emotion-aware feedback collection in remote communication, either performed by human analysts or voice-based chatbots, and can also be exploited to support the analysis of meetings in software engineering research.\n <\/jats:p>","DOI":"10.1145\/3635712","type":"journal-article","created":{"date-parts":[[2023,12,6]],"date-time":"2023-12-06T12:24:32Z","timestamp":1701865472000},"page":"1-36","update-policy":"http:\/\/dx.doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Using Voice and Biofeedback to Predict User Engagement during Product Feedback Interviews"],"prefix":"10.1145","volume":"33","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-0636-5663","authenticated-orcid":false,"given":"Alessio","family":"Ferrari","sequence":"first","affiliation":[{"name":"Consiglio Nazionale delle Ricerche, Istituto di Scienza e Tecnologie dell\u2019Informazione \u201cA. Faedo\u201d (CNR-ISTI), Pisa, Italy"}]},{"ORCID":"http:\/\/orcid.org\/0009-0009-9642-4476","authenticated-orcid":false,"given":"Thaide","family":"Huichapa","sequence":"additional","affiliation":[{"name":"Kennesaw State University (KSU), Marietta, U.S.A."}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-7922-4936","authenticated-orcid":false,"given":"Paola","family":"Spoletini","sequence":"additional","affiliation":[{"name":"Kennesaw State University (KSU), Marietta, U.S.A."}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-1160-2608","authenticated-orcid":false,"given":"Nicole","family":"Novielli","sequence":"additional","affiliation":[{"name":"University of Bari, Bari, Italy"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-0679-4361","authenticated-orcid":false,"given":"Davide","family":"Fucci","sequence":"additional","affiliation":[{"name":"Blekinge Tekniska H\u00f6gskola (BTH), Karlskrona, Sweden"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-4630-4793","authenticated-orcid":false,"given":"Daniela","family":"Girardi","sequence":"additional","affiliation":[{"name":"University of Bari, Bari, Italy"}]}],"member":"320","published-online":{"date-parts":[[2024,4,17]]},"reference":[{"issue":"6","key":"e_1_3_2_2_2","doi-asserted-by":"crossref","first-page":"568","DOI":"10.1016\/j.infsof.2014.02.001","article-title":"A systematic literature review of software requirements prioritization research","volume":"56","author":"Achimugu Philip","year":"2014","unstructured":"Philip Achimugu, Ali Selamat, Roliana Ibrahim, and Mohd Naz\u2019ri Mahrin. 2014. 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