From evolutionary ecosystem simulations to computational models of human behavior

We have a wide breadth of computational tools available today that enable a more ethical approach to the study of human cognition and behavior. We argue that the use of computer models to study evolving ecosystems provides a rich source of inspiration, as they enable the study of complex systems that change over time. Often employing a combination of genetic algorithms and agent-based models, these methods span theoretical approaches from games to complexification, nature-inspired methods from studies of self-replication to the evolution of eyes, and evolutionary ecosystems of humans, from entire economies to the effects of personalities in teamwork. The review of works provided here illustrates the power of evolutionary ecosystem simulations and how they enable new insights for researchers. They also demonstrate a novel methodology of hypothesis exploration: building a computational model that encapsulates a hypothesis of human cognition enables it to be tested under different conditions, with its predictions compared to real data to enable corroboration. Such computational models of human behavior provide us with virtual test labs in which unlimited experiments can be performed. This article is categorized under: Computer Science and Robotics > Artificial Intelligence

Modeling Crowd Feedback in the Mobile App Market

Mobile application (app) stores, such as Google Play and the Apple App Store, have recently emerged as a new model of online distribution platform. These stores have expanded in size in the past five years to host millions of apps, offering end-users of mobile software virtually unlimited options to choose from. In such a competitive market, no app is too big to fail. In fact, recent evidence has shown that most apps lose their users within the first 90 days after initial release. Therefore, app developers have to remain up-to-date with their end-users’ needs in order to survive. Staying close to the user not only minimizes the risk of failure, but also serves as a key factor in achieving market competitiveness as well as managing and sustaining innovation. However, establishing effective communication channels with app users can be a very challenging and demanding process. Specifically, users\u27 needs are often tacit, embedded in the complex interplay between the user, system, and market components of the mobile app ecosystem. Furthermore, such needs are scattered over multiple channels of feedback, such as app store reviews and social media platforms. To address these challenges, in this dissertation, we incorporate methods of requirements modeling, data mining, domain engineering, and market analysis to develop a novel set of algorithms and tools for automatically classifying, synthesizing, and modeling the crowd\u27s feedback in the mobile app market. Our analysis includes a set of empirical investigations and case studies, utilizing multiple large-scale datasets of mobile user data, in order to devise, calibrate, and validate our algorithms and tools. The main objective is to introduce a new form of crowd-driven software models that can be used by app developers to effectively identify and prioritize their end-users\u27 concerns, develop apps to meet these concerns, and uncover optimized pathways of survival in the mobile app ecosystem

Demand modeling for mobile app stores

Smartphones have reached a relatively high market share of the mobile market, creating new market opportunities. As a result, different stakeholders are investing in the mobile industry attempting to generate a higher revenue share. Hence, competition between various mobile device manufacturers has increased, as they compete for customers. These device manufacturers have created their own ecosystems, trying to lock-in their customers. These ecosystems include the application (app) stores providing services for mobile users. Currently, the two leading app stores are the Apple App Store and Google Play. Similarly, the competition exists among app developers of both stores. Therefore, it is vital to understand the user demands to design a successful app is popular in these stores. This thesis identifies successful app categories for both app stores from the perspective of an app developer. It adopts basic descriptive analysis for the dataset provided during September and October 2013 regarding the US and Finnish markets. Furthermore, it introduces a probabilistic graphical model based on Bayesian Network, aiming to understand the dynamics of mobile app stores. The thesis defines the success indicator for each category of apps, and then compares the results of both app stores. The top successful app categories in the US market include Social Networking, Productivity, Music, Finance, Education, Sports, Entertainment, and Travel. The corresponding app categories in Finland include Social Networking, Finance, Education, Music, Productivity, Entertainment, Photos and Video, Lifestyle, Games, and News. The thesis concludes that Google Play has higher success indicators than Apple App Store both in US and Finnish markets. Additionally, the success indicator is higher for free apps compared to paid apps. The results of this research contribute to recommendations for developers, during the development and publishing stages of an app, as well as building marketing strategies for mobile apps. Furthermore, it suggests a framework to identify successful apps in mobile app stores

Generating synthetic energy usage data to enable machine learning for sustainable accommodation

Machine Learning has the potential to discover new correlations between energy usage in apartments and variables such as seasonality, apartment location, size, efficiency and details of those staying in the apartments, thus helping apartments to become more sustainable and helping those who stay in them to use less energy. The biggest impedance to creating such ML tools is lack of viable data - without the data, the tools cannot be created - yet it is not feasible to wait for several years' worth of good data before creating the tools. Here we present a solution to this problem: the use of a digital twin to generate synthetic data. This approach is viable even when there is no existing data, but when expert knowledge about the relationship between systems exist. To achieve this, we develop a new agent-based synthetic data generator (ASDG) and explore a case study with a corporate housing and luxury alternate accommodation marketplace called TheSqua.re. We show that unlimited quantities of realistic data can be automatically generated, including data for different scenarios, and that it can be used by Machine Learning to discover the underlying correlations

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

A survey of app store analysis for software engineering

App Store Analysis studies information about applications obtained from app stores. App stores provide a wealth of information derived from users that would not exist had the applications been distributed via previous software deployment methods. App Store Analysis combines this non-technical information with technical information to learn trends and behaviours within these forms of software repositories. Findings from App Store Analysis have a direct and actionable impact on the software teams that develop software for app stores, and have led to techniques for requirements engineering, release planning, software design, security and testing. This survey describes and compares the areas of research that have been explored thus far, drawing out common aspects, trends and directions future research should take to address open problems and challenges

Appgazdaság : a mobilapplikációs ökoszisztéma vizsgálata = App economy : exploring the mobile app ecosystem

A mobilapplikációs piac a 2008-ban történt megszületése óta folyamatosan fejlődik. A tanulmány a mobilapplikációs ökoszisztéma mikroökonómiai tényezőinek vizsgálatára fókuszál a nemzetközi kutatási eredményeken keresztül, ami a magyar szakirodalomban hiánypótlónak tekinthető. Áttekintést ad az appgazdaság szereplőiről (felhasználók, fejlesztők és alkalmazás-áruházak) és a köztük kialakult viszonyrendszerről. Azonosítja az ökoszisztémában előállított javak jellemzőit és sikertényezőit. A mobilalkalmazások az elérendő gazdasági-társadalmi haszontól függően több szempont alapján (technológiai, motivációs, felhasználás célja, üzleti modell) csoportosíthatók. A piac jellemzően rugalmas és reszponzív a gyorsan kialakuló és hirtelen változó trendekre. E jellemzője miatt könnyebb belépni a piacra, mint ott eredményesen versenyben maradni. A tanulmány kiemeli az appgazdaságban rejlő potenciált, és rámutat arra, hogy más, nagyobb üzleti ágazatok számára miért jövedelmező az applikációfejlesztésbe való befektetés. = The mobile app market has been evolving since its birth in 2008. This study focuses on the microeconomic factors of the mobile app ecosystem through international research results. The paper can be considered a gap-filling study in the Hungarian literature. It provides an overview of the players in the app economy (users, developers, and app stores) and the relationships between them. It identifies the characteristics and success factors of the goods produced in the ecosystem. Mobile applications can be grouped according to several aspects (technological, motivational, purpose of use, business model) depending on the socio-economic benefits to be achieved. The market is typically flexible and responsive to rapidly emerging and abruptly changing trends. This characteristic makes it easier to enter the market than to compete effectively. The study highlights the potential of the app economy and shows why other, larger business sectors find it profitable to invest in app development