Data-Driven Analysis towards Monitoring Software Evolution by Continuously Understanding Changes in Users’ Needs

Ohjelmistot eivät usein vastaa käyttäjiensä odotuksia siitä huolimatta, että niiden odotetaan tarjoavan riittävä toiminnallisuus ja olevan virheettömiä. Tästä syystä ohjelmiston ylläpito on väistämätöntä ja tärkeää jokaiselle ohjelmistoyritykselle, joka haluaa pitää tuotteensa tai palvelunsa kannattavana. Koska kilpailu nykyajan ohjelmistomarkkinoilla on tiukkaa ja käyttäjien on helppo lopettaa tuotteen käyttö, yritysten on erityisen tärkeää tarkkailla ja ylläpitää käyttäjätyytyväisyyttä pitkäaikaisen menestyksen turvaamiseksi. Tämän saavuttamiseksi tärkeää on jatkuvasti ymmärtää ja kohdata käyttäjien tarpeet ja odotukset, sillä on tehokkaampaa kohdentaa ylläpito käyttäjien esittämien ongelmien perusteella. Toisaalta internet-teknologiat ovat kehittyneet nopeasti samalla, kun käyttäjien luoman sisällön määrä on kasvanut räjähdysmäisesti. Käyttäjien antama palaute (numeerinen arvostelu, ehdotus tai tekstuaalinen arvio) on esimerkki tällaisesta käyttäjien luomasta sisällöstä ja sen merkitys tuotteiden kehittämisessä asiakkaiden tarpeiden pohjalta kasvaa jatkuvasti. Käyttäjien tarpeiden ymmärtäminen on erityisen tärkeää jatkuvaa ylläpitoa ja kehitystystä vaativissa ohjelmistoissa. Tällöin on myös oleellista ymmärtää, miten asiakkaiden mielipiteet muuttuvat ajan kuluessa. Tämän lisäksi datan louhimisen ja koneoppimisen kehitys vähentävät vaivaa, joka käyttäjän tuottaman datan analysointiin ja erityisesti heidän käyttymisensä ymmärtämiseen tarvitaan. Vaikka useat tutkimukset ehdottavat tietokeskeistä lähestymistä palautteen arvioin- tiin, ohjelmiston ylläpitoa ja kehitystä hyödyntäviä lähestymistapoja on vähän. Monet menetelmät keskittyvät arvostelujen analysoinnissa tekstinlouhintaan paljastaakseen käyttäjien mielipiteet. Useat menetelmät keskittyvät myös tunnistamaan ja luokit- telemaan palautetyyppejä kuten ominaisuuspyyntöjä, virheilmoituksia ja tunteenilmauksia. Jotta ohjelmiston ylläpidosta saataisiin tehokkaampaa, tarvitaankin tehokas lähestymistapa ohjelmiston havaitun käyttäjäkokemuksen ja sen muutosten tarkkailuun ohjelmiston kehittyessä.Software products, though always being expected to provide satisfactory functionalities and be bug-free, somehow fail to meet the expectations of their users. Thus, software maintenance is inevitable and critical for any software companies who want their products or services to continue profiting. On the other hand, due to the fierce competitiveness in the contemporary software market, as well as the ease of user churns, monitoring and sustaining the satisfaction of the users is a critical criterion for the long-term success of any software products within their evolution stage. To such an end, continuously understanding and meeting the users’ needs and expectations is the key, as it is more efficient and effective to allocate maintenance effort accordingly to address the issues raised by users. On the other hand, accompanied by the rapid development of internet technologies, the volume of user-generated content has been increasing exponentially. Among such user-generated content, feedback from the customers, either numeric rating, recommendation, or textual reviews, have been playing an increasingly critical role in product designs in terms of understanding customers’ needs. Especially for software products that require constant maintenance and are continuously evolving, understanding of users’ needs and complaints, as well as the changes in their opinions through time, is of great importance. Additionally, supported by the advance of data mining and machine learning techniques, the effort of knowledge discovery from analyzing such data and specially understanding the behavior of the users shall be largely reduced. However, though many studies propose data-driven approaches for feedback analysis, the ones specifically on applying such methods supporting software maintenance and evolution are limited. Many studies focus on the text mining perspective of review analysis towards eliciting users’ opinions. Many others focus on the detection and classification of feedback types, e.g., feature requests, bug reports, and emotion expression, etc. For the purpose of enhancing the effectiveness in soft ware maintenance and evolution practice, an effective approach on the software’s perceived user experience and the monitoring of its changes during evolution is re- quired. To support the practice of software maintenance and evolution targeting enhancing user satisfaction, we propose a data-driven user review analysis approach. The contribution of this research aims to answer the following research questions: RQ1. How to analyze users’ collective expectation and perceived quality in use with data- driven approaches by exploiting sentiment and topics? RQ2. How to monitor user satisfaction over software updates during software evolution using reviews’ topics and sentiments? RQ3. How to analyze users’ profiles, software types and situational contexts as contexts of use that supports the analysis of user satisfaction? Towards answering RQ1, the thesis proposes a data-driven approach of user perceived quality evaluation and users’ needs extraction via sentiment analysis and topic modeling on large volume of user review data. Based on such outcome, the answer to RQ2 encompasses of 1) the approach to monitor user opinion changes through software evolution by detecting similar topic pairs and 2) the approach to identify the problematic updates based on anomalies in review sentiment distribution. Towards the answer to RQ3, a three-fold analysis is proposed: 1) situational contexts and ways of interaction analysis, 2) user profile and preference analysis and 3) software type and related features analysis. All the above approaches are validated by case studies. This thesis contributes to the examination of applying data-driven end user re- view analysis methods supporting software maintenance and evolution. The main implication is to enrich the existing domain knowledge of software maintenance and evolution in terms of taking advantage of the collective intelligence of end users. In addition, it conveys unique contribution to the research on software evolution con- texts in terms of various meaningful aspects and leads to a potential interdisciplinary contribution as well. On the other hand, this thesis also contributes to software maintenance and evolution practice even in the larger scope of the software industry by proposing an effective series of approaches that address critical issues within. It helps the developers ease their effort in release planning and other decision-making activities

Be Mindful of User Preferences: An Explorative Study on Game Design Elements in Mindfulness Applications

Mindfulness practices are valuable exercises for physical and mental health. Various digital applications exist that support individuals in practicing mindfulness. Following the trend of gamifying utilitarian systems, many mindfulness applications (MAs) incorporate game design elements (GDEs). However, little is known about users’ GDE preferences in this unique context. In line with extant research that investigated users’ GDE preferences in other contexts, we conducted an online survey among 168 potential users of MAs. The results indicate that users generally prefer progress, levels, and goals in MAs, while leaderboards and avatars are not highly rated. Furthermore, we identified four context-independent and three context-dependent rationales that help explain users’ GDE preferences. By providing first insights into MAs as a peculiar application context for gamification, our work contributes to advancing knowledge of contextual differences in users’ GDE preferences while challenging the extant research assumptions regarding the dominance of contextual factors in forming user preferences

Be Mindful of User Preferences: An Explorative Study on Game Design Elements in Mindfulness Applications

Mindfulness practices are valuable exercises for physical and mental health. Various digital applications exist that support individuals in practicing mindfulness. Following the trend of gamifying utilitarian systems, many mindfulness applications (MAs) incorporate game design elements (GDEs). However, little is known about users’ GDE preferences in this unique context. In line with extant research that investigated users’ GDE preferences in other contexts, we conducted an online survey among 168 potential users of MAs. The results indicate that users generally prefer progress, levels, and goals in MAs, while leaderboards and avatars are not highly rated. Furthermore, we identified four context-independent and three context-dependent rationales that help explain users’ GDE preferences. By providing first insights into MAs as a peculiar application context for gamification, our work contributes to advancing knowledge of contextual differences in users’ GDE preferences while challenging the extant research assumptions regarding the dominance of contextual factors in forming user preferences

Shallow Representations, Profound Discoveries : A methodological study of game culture in social media

This thesis explores the potential of representation learning techniques in game studies, highlighting their effectiveness and addressing challenges in data analysis. The primary focus of this thesis is shallow representation learning, which utilizes simpler model architectures but is able to yield effective modeling results. This thesis investigates the following research objectives: disentangling the dependencies of data, modeling temporal dynamics, learning multiple representations, and learning from heterogeneous data. The contributions of this thesis are made from two perspectives: empirical analysis and methodology development, to address these objectives. Chapters 1 and 2 provide a thorough introduction, motivation, and necessary background information for the thesis, framing the research and setting the stage for subsequent publications. Chapters 3 to 5 summarize the contribution of the 6 publications, each of which contributes to demonstrating the effectiveness of representation learning techniques in addressing various analytical challenges. In Chapter 1 and 2, the research objects and questions are also motivated and described. In particular, Introduction to the primary application field game studies is provided and the connections of data analysis and game culture is highlighted. Basic notion of representation learning, and canonical techniques such as probabilistic principal component analysis, topic modeling, and embedding models are described. Analytical challenges and data types are also described to motivate the research of this thesis. Chapter 3 presents two empirical analyses conducted in Publication I and II that present empirical data analysis on player typologies and temporal dynamics of player perceptions. The first empirical analysis takes the advantage of a factor model to offer a flexible player typology analysis. Results and analytical framework are particularly useful for personalized gamification. The Second empirical analysis uses topic modeling to analyze the temporal dynamic of player perceptions of the game No Man’s Sky in relation to game changes. The results reflect a variety of player perceptions including general gaming activities, game mechanic. Moreover, a set of underlying topics that are directly related to game updates and changes are extracted and the temporal dynamics of them have reflected that players responds differently to different updates and changes. Chapter 4 presents two method developments that are related to factor models. The first method, DNBGFA, developed in Publication III, is a matrix factorization model for modeling the temporal dynamics of non-negative matrices from multiple sources. The second mothod, CFTM, developed in Publication IV introduces a factor model to a topic model to handle sophisticated document-level covariates. The develeopd methods in Chapter 4 are also demonstrated for analyzing text data. Chapter 5 summarizes Publication V and Publication VI that develop embedding models. Publication V introduces Bayesian non-parametric to a graph embedding model to learn multiple representations for nodes. Publication VI utilizes a Gaussian copula model to deal with heterogeneous data in representation learning. The develeopd methods in Chapter 5 are also demonstrated for data analysis tasks in the context of online communities. Lastly, Chapter 6 renders discussions and conclusions. Contributions of this thesis are highlighted, limitations, ongoing challenges, and potential future research directions are discussed

BBC'22

The International Conference BBC'22 aims to provide an opportunity for all academic and non-academics to share their personal experiences and projects, presenting their contributions and getting feedback from other attendees.info:eu-repo/semantics/publishedVersio

Future of Online and Digital Learning in Post-Secondary Art Institutions

This research project explores the current state of online and digital learning in Post-Secondary Art (PSA) institutions and highlights some of the key challenges and opportunities for change within an institution. The paper aims to visualize possible future scenarios for learning in art institutions and provide recommendations to assist in planning for the future of these organizations. The project draws on the theories of learning, a history of transformation in higher education, elements of online learning, and current trends in the field, to build a foundation for possible futures. By using foresight methodologies, the project generates four scenarios that take readers to 2040 and provide them with alternative learning landscapes through technology

Understanding personal and contextual factors to increase motivation in gamified systems

Gamification, the use of game elements in non-game contexts, has been shown to help people reaching their goals, affect people's behavior and enhance the users' experience within interactive systems. However, past research has shown that gamification is not always successful. In fact, literature reviews revealed that almost half of the interventions were only partially successful or even unsuccessful. Therefore, understanding the factors that have an influence on psychological measures and behavioral outcomes of gamified systems is much in need. In this thesis, we contribute to this by considering the context in which gamified systems are applied and by understanding personal factors of users interacting with the system. Guided by Self-Determination Theory, a major theory on human motivation, we investigate gamification and its effects on motivation and behavior in behavior change contexts, provide insights on contextual factors, contribute knowledge on the effect of personal factors on both the perception and effectiveness of gamification elements and lay out ways of utilizing this knowledge to implement personalized gamified systems. Our contribution is manifold: We show that gamification affects motivation through need satisfaction and by evoking positive affective experiences, ultimately leading to changes in people's behavior. Moreover, we show that age, the intention to change behavior, and Hexad user types play an important role in explaining interpersonal differences in the perception of gamification elements and that tailoring gamified systems based on these personal factors has beneficial effects on both psychological and behavioral outcomes. Lastly, we show that Hexad user types can be partially predicted by smartphone data and interaction behavior in gamified systems and that they can be assessed in a gameful way, allowing to utilize our findings in gamification practice. Finally, we propose a conceptual framework to increase motivation in gamified systems, which builds upon our findings and outlines the importance of considering both contextual and personal factors. Based on these contributions, this thesis advances the field of gamification by contributing knowledge to the open questions of how and why gamification works and which factors play a role in this regard.Gamification, die Nutzung von Spielelementen in spielfremden Kontexten, kann nachweislich Menschen helfen, ihre Ziele zu erreichen, das Verhalten von Menschen zu beeinflussen und die Erfahrung der User in interaktiven Systemen zu verbessern. Allerdings hat die bisherige Forschung gezeigt, dass Gamification nicht immer erfolgreich ist. So haben Literaturübersichten ergeben, dass fast die Hälfte der Interventionen nur teilweise erfolgreich oder sogar erfolglos waren. Daher besteht ein großer Bedarf, die Faktoren zu verstehen, die einen Einfluss auf psychologische Maße sowie auf das Verhalten von Usern in gamifizierten Systemen haben. In dieser Arbeit tragen wir dazu bei, indem wir den Kontext, in dem gamifizierte Systeme eingesetzt werden, betrachten und persönliche Faktoren von Usern, die mit dem System interagieren, verstehen. Geleitet von der Selbstbestimmungstheorie, einer der wichtigsten Theorien zur menschlichen Motivation, untersuchen wir Gamification und dessen Auswirkungen auf Motivation und Verhalten in Kontexten zur Verhaltensänderung. Wir liefern Erkenntnisse über kontextuelle Faktoren, tragen Wissen über den Einfluss persönlicher Faktoren auf die Wahrnehmung und Effektivität von Gamification-Elementen bei und bieten Möglichkeiten, dieses Wissen für die Implementierung personalisierter gamifizierter Systeme zu nutzen. Unser Beitrag ist mannigfaltig: Wir zeigen, dass Gamification die Motivation durch Bedürfnisbefriedigung und durch das Hervorrufen positiver affektiver Erfahrungen beeinflusst, was letztlich zu Verhaltensänderungen führen kann. Darüber hinaus zeigen wir, dass das Alter, die Absicht, das Verhalten zu ändern, und Hexad-Usertypen eine wichtige Rolle bei der Erklärung von interpersonellen Unterschieden in der Wahrnehmung von Gamification-Elementen spielen. Ebenso zeigen unsere Resultate dass die Anpassung von gamifizierten Systemen auf Basis dieser persönlichen Faktoren positive Auswirkungen auf psychologische und verhaltensbezogene Ergebnisse hat. Letztlich zeigen wir, dass Hexad-Usertypen teilweise durch Smartphone-Daten und Interaktionsverhalten in gamifizierten Systemen vorhergesagt werden können und dass sie auf spielerische Art und Weise erhoben werden können. Dies ermöglicht, unsere Erkenntnisse in der Gamification-Praxis zu nutzen. Auf Basis dieser Ergebnisse schlagen wir ein konzeptuelles Framework zur Steigerung der Motivation in gamifizierten Systemen vor, das die Wichtigkeit der Berücksichtigung sowohl kontextueller als auch persönlicher Faktoren hervorhebt. Diese Erkenntnisse bereichern das Forschungsfeld Gamification, indem sie Wissen zu den offenen Fragen, wie und warum Gamification funktioniert und welche Faktoren in diesem Zusammenhang eine Rolle spielen, beitragen

The Impact of Community Involvement on Game Life-Cycle: Evidence based on Gaming Platform Steam

Later stages of the product life-cycle are characterized by diminishing sales and declining prices. Especially firms with substantial product development costs, as is the case in the video game industry, are dependent on long product life-cycles to amortize initial costs. This confronts firms with the fundamental challenge of maintaining the value of their product from the consumer’s perspective and thus delaying the natural price decline. We investigate whether product features that facilitate community involvement and interaction are an effective means to keep the product stimulating and relevant in the long run. Using extensive data from the PC video game market, we show that the inclusion of interactive, community-engaging features allows firms to both charge higher prices and delay the natural price decline of their product. However, for one of the investigated features we find the opposite effect, which we explain by subsequent analysis. Thereby, we gain valuable insights into the importance of robustly designed incentive systems in community-focused features. Our findings could help firms in their efforts to design attractive and economically viable products with prolonged life-cycles. Keywords: Product life-cycle; digital goods pricing; user communities; co-creation; digital gaming platforms.Later stages of the product life-cycle are characterized by diminishing sales and declining prices. Especially firms with substantial product development costs, as is the case in the video game industry, are dependent on long product life-cycles to amortize initial costs. This confronts firms with the fundamental challenge of maintaining the value of their product from the consumer’s perspective and thus delaying the natural price decline. We investigate whether product features that facilitate community involvement and interaction are an effective means to keep the product stimulating and relevant in the long run. Using extensive data from the PC video game market, we show that the inclusion of interactive, community-engaging features allows firms to both charge higher prices and delay the natural price decline of their product. However, for one of the investigated features we find the opposite effect, which we explain by subsequent analysis. Thereby, we gain valuable insights into the importance of robustly designed incentive systems in community-focused features. Our findings could help firms in their efforts to design attractive and economically viable products with prolonged life-cycles. Keywords: Product life-cycle; digital goods pricing; user communities; co-creation; digital gaming platforms