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Context Manual graphical user interface (GUI) software testing presents a substantial part of the overall practiced testing efforts, despite various research efforts to further increase test automation. Augmented Testing (AT), a novel approach for GUI testing, aims to aid manual GUI-based testing through a tool-supported approach where an intermediary visual layer is rendered between the system under test (SUT) and the tester, superimposing relevant test information. Objective The primary objective of this study is to gather empirical evidence regarding AT’s efficiency compared to manual GUI-based regression testing. Existing studies involving testing approaches under the AT definition primarily focus on exploratory GUI testing, leaving a gap in the context of regression testing. As a secondary objective, we investigate AT’s benefits, drawbacks, and usability issues when deployed with the demonstrator tool, Scout. Method We conducted an experiment involving 13 industry professionals, from six companies, comparing AT to manual GUI-based regression testing. These results were complemented by interviews and Bayesian data analysis (BDA) of the study’s quantitative results. Results The results of the Bayesian data analysis revealed that the use of AT shortens test durations in 70% of the cases on average, concluding that AT is more efficient. When comparing the means of the total duration to perform all tests, AT reduced the test duration by 36% in total. Participant interviews highlighted nine benefits and eleven drawbacks of AT, while observations revealed four usability issues. Conclusion This study presents empirical evidence of improved efficiency using AT in the context of manual GUI-based regression testing. We further report AT’s benefits, drawbacks, and usability issues. The majority of identified usability issues and drawbacks can be attributed to the tool implementation of AT and, thus, can serve as valuable input for future tool development.

Despite the growing application of augmented reality in advertising, there is limited understanding about how customers respond to their interaction with the augmented reality advertising and how it differs from a standard paper-based advertising. Augmented reality ads are immersive, interactive, and lifelike, which means they may help companies create an emotional connection with their customers. The authors test if customers would respond in terms of emotional and affective intensity differently to augmented reality versus standard paper-based advertising. The results of two laboratory studies that consider physiological measures of arousal (galvanic skin response), self-reported measure of affect intensity and willingness to pay show that the higher willingness to pay for customers exposed to augmented reality as opposed to standard paper-based ads is driven by the physiological arousal, but not by the self-reported affect intensity and that processing fluency possibly underlies consumer’s enhanced emotional responses toward AR. These results suggest that replacing traditional advertisements with augmented reality advertisements enhances customer physiological responses and willingness to pay, with possible implications on customer segmentation and marketing communication.

Visualizing molecular conformations and complex compound structures and chemical transformations in 3D is one of the most difficult tasks for undergraduate chemistry students. Modern computational technologies have revolutionized every aspect of our lives, including education. As a result, many researchers and educators are working on enhancing student learning and improving construction of knowledge by employing technologies that better illustrate theoretical concepts, such as the visualization of molecular geometry in chemistry. Here, to aid students in understanding molecular structures and chemical reaction mechanisms at the molecular level, we initially developed several 3D animations of fundamental chemical transformations aimed at organic chemistry courses for second- and third-year undergraduate level. These animations became the basis for the 3D augmented reality tool called ARchemy. A comprehensive survey was conducted to gather student feedback on the effectiveness of these tools and their perception of the subject matter using these technologies, which will be presented in this project.

The highly demanding safety standards adopted in the railway context imply that cutting-edge technologies must limit accidents. This paper presents the human-centered outcomes of the VRAIL project, an industrial research project aiming to use enabling technologies and develop methodologies for operators directly involved in infrastructure management in the railway field. Developing integrated monitoring systems and applications that exploit Augmented Reality (AR) and Virtual Reality (VR) becomes crucial to support the awareness of planning and maintenance operators required to comply with high-quality standards. This paper addresses the abovementioned issue by proposing the development of two different prototype applications in both AR and VR for railway infrastructure data management. These environments will provide the planning operator with a complete platform to explore, use to plan maintenance interventions, and gather detailed reports to improve the overall safety of the railway line effectively.

The research on augmented reality applications in education is still in an early stage, and there is a lack of research on the effects and implications of augmented reality in the field of education. The purpose of this research was to measure and understand the impact of an augmented reality mobile application on the learning motivation of undergraduate health science students at the University of Cape Town. We extend previous research that looked specifically at the impact of augmented reality technology on student learning motivation. The intrinsic motivation theory was used to explain motivation in the context of learning. The attention, relevance, confidence, and satisfaction (ARCS) model guided the understanding of the impact of augmented reality on student motivation, and the Instructional Materials Motivation Survey was used to design the research instrument. The research examined the differences in student learning motivation before and after using the augmented reality mobile application. A total of 78 participants used the augmented reality mobile application and completed the preusage and postusage questionnaires. The results showed that using an augmented reality mobile application increased the learning motivation of students. The attention, satisfaction, and confidence factors of motivation were increased, and these results were found to be significant. Although the relevance factor showed a decrease it proved to be insignificant.

In this paper, we introduce LagunAR, a mobile outdoor Augmented Reality (AR) application for providing heritage information and 3D visualization on a city scale. The LagunAR application was developed to provide historical information about the city of La Laguna in the XVI century, when it was the main city in the Canary Islands. The application provides a reconstructed 3D model of the city at that time that is shown on a mobile phone over-imposed on the actual city using geolocation. The geolocated position is used also for providing information of several points of interest in the city. The paper describes the design and implementation of the application and details the optimization techniques that have been used to manage the full information of the city using a mobile phone as a sensor and visualization tool. We explain the application usability study carried out using a heuristic test; in addition it is probed by users in a qualitative user test developed as preliminary research. Results show that it is possible to develop a real-time application that shows the user a city-scale 3D model and also manages the information of the points of interest.

The use of Extended Reality (XR) (i.e. Virtual and Augmented Reality) for nutrition education and behavior change has not been comprehensively reviewed. This paper presents findings from a scoping review of current published research. Articles (n = 92) were extracted from PubMed and Scopus using a structured search strategy and selection approach. Pertinent study information was extracted using a standardized data collection form. Each article was independently reviewed and coded by two members of the research team, who then met to resolve any coding discrepancies. There is an increasing trend in publication in this area, mostly regarding Virtual Reality. Most studies used developmental testing in a lab setting, employed descriptive or observational methods, and focused on momentary behavior change like food selection rather than education. The growth and diversity of XR studies suggest the potential of this approach. There is a need and opportunity for more XR technology focused on children and other foundational theoretical determinants of behavior change to be addressed within nutrition education. Our findings suggest that XR technology is a burgeoning approach in the field of nutrition, but important gaps remain, including inadequate methodological rigor, community application, and assessment of the impact on dietary behaviors.

This paper proposes a new framework for leak diagnosis in pipelines using leak-augmented scalograms and deep learning. Acoustic emission (AE) scalogram images obtained from the continuous wavelet transform have been useful for pipeline health diagnosis, particularly when combined with deep learning. However, background noise has a significant impact on AE signals, which can reduce the accuracy of pipeline health identification using classification models. To address this issue, a new type of scalograms called leak-augmented scalogram is introduced, which enhances the variation in colour intensities of AE scalogram images. The leak-augmented scalograms are obtained by pre-processing them using image-enhancing Gaussian and Laplacian filters. The proposed method utilizes convolutional neural networks (CNNs) and convolutional autoencoders (CAEs) for feature extraction. The CNN extracts patterns specific to local changes, while the CAE extracts holistic patterns from the leak-augmented scalograms. The resulting leak susceptible and leak holistic indicators are merged into a single feature pool and provided as input to a shallow artificial neural network (ANN) to evaluate pipeline health conditions. The proposed method achieves high classification as well as accuracy, precision, F-1 Score and recall, compared to existing state of the art methods.

In this work, we present a system architecture that is especially designed for remote ultrasound testing inspections (UT). The system itself is realized using a real-time session service and a web service based on a REST API (REST: Representational State Transfer; API: application programming interface). This web service is used to store data persistently, e.g., sample geometries, raw nondestructive testing (NDT) data and derived inspection results in a shared dataspace. In the current development state, the results consist of textures mapped onto the sample geometry. This approach allows us to display the UT results directly on the real sample using mixed reality technologies. We also implemented a feature to assist the inspector remotely by making use of the availability of this digital representation. Hence, it is necessary to share additional data like the current UT-signal, temporary position marks, user and device positions, etc. between the different participants. A real-time distribution of this highly dynamic data is required to create an effective assistance environment. Therefore, a separate session service is used. The inspection data generated in this temporary session can also be transferred to the afore mentioned dataspace to be saved persistently. The system features mixed reality visualization for the inspector and optionally a virtual reality or a 3D Desktop environment for one or more remote assistants.

Location-based augmented reality technology for real-world, outdoor experiences is rapidly gaining in popularity in a variety of fields such as engineering, education, and gaming. By anchoring medias to geographic coordinates, it is possible to design immersive experiences remotely, without necessitating an in-depth knowledge of the context. However, the creation of such experiences typically requires complex programming tools that are beyond the reach of mainstream users. We introduce BiodivAR, a web cartographic tool for the authoring of location-based AR experiences. Developed using a user-centered design methodology and open-source interoperable web technologies, it is the second iteration of an effort that started in 2016. It is designed to meet needs defined through use cases co-designed with end users and enables the creation of custom geolocated points of interest. This approach enabled substantial progress over the previous iteration. Its reliance on geolocation data to anchor augmented objects relative to the user’s position poses a set of challenges: On mobile devices, GNSS accuracy typically lies between 1 m and 30 m. Due to its impact on the anchoring, this lack of accuracy can have deleterious effects on usability. We conducted a comparative user test using the application in combination with two different geolocation data types (GNSS versus RTK). While the test’s results are undergoing analysis, we hereby present a methodology for the assessment of our system’s usability based on the use of eye-tracking devices, geolocated traces and events, and usability questionnaires.