On the Road to Enriching the App Improvement Process with Emotions

Scherr, Simon André; Patrick, Meriel; Kammler, Christian; Elberzhager, Frank

doi:10.1109/rew.2019.00018

Cited by 7 publications

(5 citation statements)

References 14 publications

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“…Fuzzy cognitive models have analyzed relationships between customer emotions, perceptual patterns, and product design elements, enabling emotion-focused product optimization ( Dong, Guo & Liu, 2014 ). Facial expression mapping to emotions via camera data has been proposed to enrich software requirements validation and verification, but further evaluation is needed ( Scherr et al, 2019 ). Analyzing users’ affective reactions to requirements and prototypes has been suggested to trace the source of emotions, improve specifications to remove negative emotion triggers, reassure users, and transform negative emotions through system responses in applications like healthcare ( Sutcliffe, 2011 ).…”

Section: Resultsmentioning

confidence: 99%

Eliciting and modeling emotional requirements: a systematic mapping review

Alkhomsan,

Baslyman,

Alshayeb

2024

PeerJ Computer Science

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Context Considering users’ emotions plays an extremely crucial role in the adoption and acceptance of recent technology by the end user. User emotions can also help to identify unknown requirements, saving resources that would otherwise be wasted if discovered later. However, eliciting and modeling users’ emotional requirements in software engineering is still an open research area. Objective This systematic mapping review analyzes emotional requirements (ER) practices in software engineering from two perspectives: elicitation and modeling. For elicitation techniques, we investigate the techniques, evaluation methods, limitations, and application domains. For modeling techniques, we examine the modeling languages, analyses, limitations, and domains. Method We systematically reviewed studies on emotional requirements engineering published between 1993–2023 and identified 46 relevant primary studies. Results A total of 34 studies investigated ER elicitation techniques, five examined modeling techniques, and seven covered both. Illustrative case studies were the main evaluation method for proposed elicitation techniques. Identified limitations include time consumption and extensive human involvement. The dominant application domains were healthcare and well-being, and game development. Conclusion This review summarizes the current landscape of emotional requirements research, highlighting key elicitation and modeling techniques, evaluations, limitations, and domains. Further research can build on these findings to advance emotional requirements practices in software engineering. Future research may address (1) managing conflicting emotional requirements across users, (2) evaluating the value and impact of considering emotional requirements during the development and (3) Modeling and analyzing emotional requirements in relation to other requirements.

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Section: Resultsmentioning

confidence: 99%

Eliciting and modeling emotional requirements: a systematic mapping review

Alkhomsan,

Baslyman,

Alshayeb

2024

PeerJ Computer Science

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“…A total of 30,000 infrared dots are projected onto the target object and captured by an infrared-sensitive camera, allowing fine details such as contours and recesses to be detected by the camera [65]. This granularity has enabled its use in a wide range of applications, ranging from the analysis of human facial emotions to the customization of prosthetics and medical equipment requiring precise fit [63,[66][67][68][69][70][71].…”

Section: Facial Morphometrics and Malnutrition Screeningmentioning

confidence: 99%

Use of Facial Morphology to Determine Nutritional Status in Older Adults: Opportunities and Challenges

Tay¹,

Quek²,

Kaur³

et al. 2022

JMIR Public Health Surveill

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Undiagnosed malnutrition is a significant problem in high-income countries, which can reduce the quality of life of many individuals, particularly of older adults. Moreover, it can also inflate the costs of existing health care systems because of the many metabolic complications that it can cause. The current methods for assessing malnutrition can be cumbersome. A trained practitioner must be present to conduct an assessment, or patients must travel to facilities with specialized equipment to obtain their measurements. Therefore, digital health care is a possible way of closing this gap as it is rapidly gaining traction as a scalable means of improving efficiency in the health care system. It allows for the remote monitoring of nutritional status without requiring the physical presence of practitioners or the use of advanced medical equipment. As such, there is an increasing interest in expanding the range of digital applications to facilitate remote monitoring and management of health issues. In this study, we discuss the feasibility of a novel digital remote method for diagnosing malnutrition using facial morphometrics. Many malnutrition screening assessments include subjective assessments of the head and the face. Facial appearance is often used by clinicians as the first point of qualitative indication of health status. Hence, there may be merit in quantifying these subtle but observable changes using facial morphometrics. Modern advancements in artificial intelligence, data science, sensors, and computing technologies allow facial features to be accurately digitized, which could potentially allow these previously intuitive assessments to be quantified. This study aims to stimulate further discussion and discourse on how this emerging technology can be used to provide real-time access to nutritional status. The use of facial morphometrics extends the use of currently available technology and may provide a scalable, easily deployable solution for nutritional status to be monitored in real time. This will enable clinicians and dietitians to keep track of patients remotely and provide the necessary intervention measures as required, as well as providing health care institutions and policy makers with essential information that can be used to inform and enable targeted public health approaches within affected populations.

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“…With respect to using biofeedback and voice analysis in software engineering and RE, our study is among the first ones to specifically focus on users' interviews rather than product usage or development tasks. Previous studies (e.g., Scherr et al (2019b);Mennig et al (2019)) focus on detecting the user's engagement experienced while using the software features. In our case, we aim to detect users' engagement about certain features when users reflect on the features and speak about them.…”

Section: Sentiment and Emotions In Requirements Engineeringmentioning

confidence: 99%

Using Voice and Biofeedback to Predict User Engagement during Requirements Interviews

Ferrari¹,

Huichapa²,

Spoletini³

et al. 2021

Preprint

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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 are 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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On the Road to Enriching the App Improvement Process with Emotions

Cited by 7 publications

References 14 publications

Eliciting and modeling emotional requirements: a systematic mapping review

Eliciting and modeling emotional requirements: a systematic mapping review

Use of Facial Morphology to Determine Nutritional Status in Older Adults: Opportunities and Challenges

Using Voice and Biofeedback to Predict User Engagement during Requirements Interviews

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