Sorterius: Game-inspired App for Encouraging Outdoor Physical Activity for People with Intellectual Disabilities

People with intellectual disabilities have difficulties in reaching the World Health Organization's (WHO) suggested level of physical activity. Previous research shows that participating in physical activities often is related to self-efficacy in a physical activity setting and personal motivation. As physical activity has significant effects on physical and mental health, this thesis aimed to develop a mobile application that could help people with intellectual disabilities be more physically active. In the process of creating an encouraging and user-friendly mobile application, this project includes literature reviews, meetings with experts in the field, discussions with special education teachers and teachers working with people with intellectual disability, and the author's own experience with this user group. The project relies on guidelines and theory to create a user interface to fit people with intellectual disabilities. This thesis presents a cross-platform mobile application that combines the digital and real world. Using augmented reality, players walk around in the real world looking after digital garbage. As they walk, they will find garbage on the ground and get the option of sorting the garbage in the correct garbage bins. The game's main objective is to look for garbage and throw it in the correct garbage bin. As users progress throughout the game, they earn stars based on their step count while playing the game. Together with family members or assistants, they can add weekly physical activity goals and earn special rewards created by family members or assistants. Usability testing is mainly done on special education teachers, social workers, psychologist, and researchers working with people with intellectual disabilities. It revealed that creating a mobile application focusing on everyday life scenarios can have a potential value for the targeted user group. However, testing also showed that using augmented reality can be challenging. Long-term testing on individuals with an intellectual disability will start in the upcoming weeks in a study conducted by the University Hospital of North Norway (UNN), in collaboration with UiT The Arctic University of Norway

Mining app reviews to support software engineering

The thesis studies how mining app reviews can support software engineering. App reviews —short user reviews of an app in app stores— provide a potentially rich source of information to help software development teams maintain and evolve their products. Exploiting this information is however difficult due to the large number of reviews and the difficulty in extracting useful actionable information from short informal texts. A variety of app review mining techniques have been proposed to classify reviews and to extract information such as feature requests, bug descriptions, and user sentiments but the usefulness of these techniques in practice is still unknown. Research in this area has grown rapidly, resulting in a large number of scientific publications (at least 182 between 2010 and 2020) but nearly no independent evaluation and description of how diverse techniques fit together to support specific software engineering tasks have been performed so far. The thesis presents a series of contributions to address these limitations. We first report the findings of a systematic literature review in app review mining exposing the breadth and limitations of research in this area. Using findings from the literature review, we then present a reference model that relates features of app review mining tools to specific software engineering tasks supporting requirements engineering, software maintenance and evolution. We then present two additional contributions extending previous evaluations of app review mining techniques. We present a novel independent evaluation of opinion mining techniques using an annotated dataset created for our experiment. Our evaluation finds lower effectiveness than initially reported by the techniques authors. A final part of the thesis, evaluates approaches in searching for app reviews pertinent to a particular feature. The findings show a general purpose search technique is more effective than the state-of-the-art purpose-built app review mining techniques; and suggest their usefulness for requirements elicitation. Overall, the thesis contributes to improving the empirical evaluation of app review mining techniques and their application in software engineering practice. Researchers and developers of future app mining tools will benefit from the novel reference model, detailed experiments designs, and publicly available datasets presented in the thesis

Augmented Reality for the Mobile Police Force

Portuguese law enforcement organizations currently face a significant technology gap. Research has shown that some law organizations, such as Polícia Judiciária (PJ) and Polícia de Segurança Pública (PSP), often criticize the lack of technology to support the Police work in all kinds of fields, from criminality prevention to minor infractions. This study aims to determine how augmented reality (AR) technology can be used to ease/improve the day-to-day tasks of the law enforcement forces and how do the end-users perceive this new type of information. Based on a review of the literature and implementations on AR technologies to aid law enforcement organizations, a proof-of-concept smartphone application was developed in order to aid the police infraction ticket issuing process. The developed solution was analysed to infer its usability. As such, various users tests were conducted with a total of thirty users including police personnel and nonassociated users. The users were then asked to answer a questionnaire contemplating the System Usability Scale (SUS) questions. The responses were analysed and then combined with the Quantitative Evaluation Framework (QEF) in order to extrapolate the proof-of-concept’s final value. The results suggest that the ticket issuing process was fully integrated in the proofof-concept and was well received amongst the users. The system contemplates the possibility to scale to other devices other than the smartphone, for example surveillance cameras or wearables, as well as including new features to perform different tasks such as recognizing vehicles through AR and Depth.A introdução da tecnologia na sociedade alterou o paradigma de como as tarefas são realizadas. Uma grande parte dos setores económicos decidiu apostar na automatização e mecanização de processos, reduzindo assim os encargos em recursos humanos, bem como o número de erros humanos. Apesar do uso de tecnologias ser recorrente em várias áreas e serviços, as forças policiais continuam a ter o seu uso negligenciado. Muitas das tarefas policiais, como a passagem de infrações ou contra-ordenações, são feitas através da introdução manual de dados num sistema generalizado implantado num computador de bordo ou através da passagem de uma contra-ordenação por escrito que é posteriormente introduzida no sistema aquando a chegada do agente à esquadra do seu destacamento. O processo em questão acaba por ser bastante moroso, como também propenso à realização de erros humanos. O descontentamento das Forças policiais como a Polícia Judiciária (PJ) ou a Polícia de Segurança Pública (PSP) é visível nas contestações feitas relacionadas com a falta de suporte tecnológico em variadas operações policiais. Este estudo tem como objetivo determinar como as tecnologias de Augmented Reality (AR) podem ser utilizadas de modo a otimizar as tarefas policiais e como a introdução da mesma é percecionada pelos utilizadores nas tarefas em questão. A investigação foca-se no desenvolvimento de uma aplicação de AR para smartphone como uma prova de conceito com o intuito de assistir as forças policiais na passagem de infrações e contra-ordenações. Consequentemente, foi realizada uma investigação sobre a tecnologia de AR e as suas categorias. Após serem detalhadas as nuances da AR, foi efetuada uma investigação na literatura e implementações de trabalhos relacionados contendo sistemas que implementam AR com o objetivo primário de assistir as forças policiais em variadas tarefas. Desta forma, foi possível detalhar algumas das possíveis tecnologias que acabaram por ser utilizadas para o desenvolvimento da aplicação supramencionada. Aquando da finalização do estudo dos trabalhos relacionados, foi analisado o contexto de negócio da prova de conceito a desenvolver, validando a necessidade e o contexto onde o sistema desenvolvido viria ser inserido. A aplicação foi desenvolvida em Unity com recurso à framework ARFoundation, que possibilitou o incorporamento e sobreposição de dados virtuais sobre a vista real observada pelo utilizador. O sistema foi desenhado de forma a realizar a deteção automática de matrículas expondo a informação detetada na forma de componentes AR no ecrã do utilizador, possibilitando a posterior submissão de uma infração se a matrícula selecionada pelo utilizador estiver contida na base de dados. A prova de conceito é composta por seis conceitos de negócio, sendo estes: a interface gráfica para o utilizador; o módulo de Optical Character Recognition (OCR), responsável pela deteção e comparação de carateres alfanuméricos pre-registados no sistema: o Plate Recognition Training, responsável pelo aprendizagem dos contornos e localizações das matrículas; a câmara, responsável pela obtenção do vídeo em tempo real para deteção; o integration system, responsável por integrar todos os módulos supramencionados; e por último, o resources/fileSystem, responsável por armazenar todos os dados necessários para o funcionamento da aplicação. Após a implementação do sistema, o mesmo foi submetido a vários testes de utilizador com recurso a um conjunto predefinido de ações, de modo a aferir a integração e usabilidade da aplicação. Foram feitos testes com trinta utilizadores, incluindo alguns agentes policiais. Posteriormente, os utilizadores supracitados foram convidados à realização de um questionário. As respostas foram analisadas de forma a apurar o valor de usabilidade final referente à prova de conceito. Os resultados obtidos confirmam que o processo de submissão de infrações foi totalmente integrado na prova de conceito e que o sistema foi positivamente avaliado pelos utilizadores. O sistema contempla a possibilidade de ser integrado em diferentes dispositivos em adição ao smartphone, como por exemplo câmaras de videovigilância ou wearables. O sistema está ainda preparado para ser escalado e incluir novas funcionalidades para realizar diferentes tarefas policiais, como a de reconhecer veículos através de AR e Profundidade, sendo que este conceito foi brevemente explorado nesta tese

Rakenduste kasutajaarvustustest informatsiooni kaevandamine tarkvara arendustegevuste soodustamiseks

Kasutajate vajaduste ja ootuste hindamine on arendajate jaoks oluline oma tarkvararakenduste kvaliteedi parandamiseks. Mobiilirakenduste platvormidele sisestatud arvustused on kasulikuks infoallikaks kasutajate pidevalt muutuvate vajaduste hindamiseks. Igapäevaselt rakenduste platvormidele esitatud arvustuste suur maht nõuab aga automaatseid meetodeid neist kasuliku info leidmiseks. Arvustuste automaatseks liigitamiseks, nt veateatis või uue funktsionaalsuse küsimine, saab kasutada teksti klassifitseerimismudeleid. Rakenduse funktsioonide automaatne kaevandamine arvustustest aitab teha kokkuvõtteid kasutajate meelsusest rakenduse olemasolevate funktsioonide osas. Kõigepealt eksperimenteerime erinevate tekstiklassifitseerimise mudelitega ning võrdleme lihtsaid, leksikaalseid tunnuseid kasutavaid mudeleid keerukamatega, mis kasutavad rikkalikke lingvistilisi tunnuseid või mis põhinevad tehisnärvivõrkudel. Erinevate faktorite mõju uurimiseks funktsioonide kaevandamise meetoditele me teeme kõigepealt kindlaks erinevate meetodite baastaseme täpsuse rakendades neid samades eksperimentaalsetes tingimustes. Seejärel võrdleme neid meetodeid erinevates tingimustes, varieerides treenimiseks kasutatud annoteeritud andmestikke ning hindamismeetodeid. Kuna juhendatud masinõppel baseeruvad kaevandamismeetodid on võrreldes reeglipõhistega tundlikumad (1) andmete märgendamisel kasutatud annoteerimisjuhistele ning (2) märgendatatud andmestiku suurusele, siis uurisime nende faktorite mõju juhendatud masinõppe kontekstis ja pakkusime välja uued annoteerimisjuhised, mis võivad aidata funktsioonide kaevandamise täpsust parandada. Käesoleva doktoritöö projekti tulemusel valmis ka kontseptuaalne tööriist, mis võimaldab konkureerivaid rakendusi omavahel võrrelda. Tööriist kombineerib arvustuse tekstide klassifitseerimise ja rakenduse funktsioonide kaevandamise meetodid. Tööriista hinnanud kümme tarkvaraarendajat leidsid, et sellest võib olla kasu rakenduse kvaliteedi parandamiselFor app developers, it is important to continuously evaluate the needs and expectations of their users to improve app quality. User reviews submitted to app marketplaces are regarded as a useful information source to re-access evolving user needs. The large volume of user reviews received every day requires automatic methods to find such information in user reviews. Text classification models can be used to categorize review information into types such as feature requests and bug reports, while automatic app feature extraction from user reviews can help in summarizing users’ sentiments at the level of app features. For classifying review information, we perform experiments to compare the performance of simple models using only lexical features to models with rich linguistic features and models built on deep learning architectures, i.e., Convolutional Neural Network (CNN). To investigate factors influencing the performance of automatic app feature extraction methods, i.e. rule-based and supervised machine learning, we first establish a baseline in a single experimental setting and then compare the performances in different experimental settings (i.e., varying annotated datasets and evaluation methods). Since the performance of supervised feature extraction methods is more sensitive than rule- based methods to (1) guidelines used to annotate app features in user reviews and (2) the size of the annotated data, we investigate their impact on the performance of supervised feature extraction models and suggest new annotation guidelines that have the potential to improve feature extraction performance. To make the research results of the thesis project also applicable for non-experts, we developed a proof-of-concept tool for comparing competing apps. The tool combines review classification and app feature extraction methods and has been evaluated by ten developers from industry who perceived it useful for improving the app quality.  https://www.ester.ee/record=b529379

EFFECTIVE METHODS AND TOOLS FOR MINING APP STORE REVIEWS

Research on mining user reviews in mobile application (app) stores has noticeably advanced in the past few years. The main objective is to extract useful information that app developers can use to build more sustainable apps. In general, existing research on app store mining can be classified into three genres: classification of user feedback into different types of software maintenance requests (e.g., bug reports and feature requests), building practical tools that are readily available for developers to use, and proposing visions for enhanced mobile app stores that integrate multiple sources of user feedback to ensure app survivability. Despite these major advances, existing tools and techniques still suffer from several drawbacks. Specifically, the majority of techniques rely on the textual content of user reviews for classification. However, due to the inherently diverse and unstructured nature of user-generated online textual reviews, text-based review mining techniques often produce excessively complicated models that are prone to over-fitting. Furthermore, the majority of proposed techniques focus on extracting and classifying the functional requirements in mobile app reviews, providing a little or no support for extracting and synthesizing the non-functional requirements (NFRs) raised in user feedback (e.g., security, reliability, and usability). In terms of tool support, existing tools are still far from being adequate for practical applications. In general, there is a lack of off-the-shelf tools that can be used by researchers and practitioners to accurately mine user reviews. Motivated by these observations, in this dissertation, we explore several research directions aimed at addressing the current issues and shortcomings in app store review mining research. In particular, we introduce a novel semantically aware approach for mining and classifying functional requirements from app store reviews. This approach reduces the dimensionality of the data and enhances the predictive capabilities of the classifier. We then present a two-phase study aimed at automatically capturing the NFRs in user reviews. We also introduce MARC, a tool that enables developers to extract, classify, and summarize user reviews