Mobile application (app) reviews contain valuable information for app developers. A plethora of supervised and unsupervised techniques have been proposed in the literature to synthesize useful user feedback from app reviews. However, traditional supervised classification algorithms require extensive manual effort to label ground truth data, while unsupervised text mining techniques, such as topic models, often produce suboptimal results due to the sparsity of useful information in the reviews. To overcome these limitations, in this paper, we propose a fully automatic and unsupervised approach for extracting useful information from mobile app reviews. The proposed approach is based on keyATM, a keyword-assisted approach for generating topic models. keyATM overcomes the problem of data sparsity by using seeding keywords extracted directly from the review corpus. These keywords are then used to generate meaningful domain-specific topics. Our approach is evaluated over two datasets of mobile app reviews sampled from the domains of Investing and Food Delivery apps. The results show that our approach produces significantly more coherent topics than traditional topic modeling techniques.

Modern application stores enable developers to classify their apps by choosing from a set of generic categories, or genres, such as health, games, and music. These categories are typically static—new categories do not necessarily emerge over time to reflect innovations in the mobile software landscape. With thousands of apps classified under each category, locating apps that match a specific consumer interest can be a challenging task. To overcome this challenge, in this article, we propose an automated approach for classifying mobile apps into more focused categories of functionally related application domains. Our aim is to enhance apps visibility and discoverability. Specifically, we employ word embeddings to generate numeric semantic representations of app descriptions. These representations are then classified to generate more cohesive categories of apps. Our empirical investigation is conducted using a dataset of 600 apps, sampled from the Education, Health&Fitness, and Medical categories of the Apple App Store. The results show that our classification algorithms achieve their best performance when app descriptions are vectorized using GloVe, a count-based model of word embeddings. Our findings are further validated using a dataset of Sharing Economy apps and the results are evaluated by 12 human subjects. The results show that GloVe combined with Support Vector Machines can produce app classifications that are aligned to a large extent with human-generated classifications.

Applications of the Digital Sharing Economy (DSE), such as Uber, Airbnb, and TaskRabbit, have become a main facilitator of economic growth and shared prosperity in modern-day societies. However, recent research has revealed that the participation of minority groups in DSE activities is often hindered by different forms of bias and discrimination. Evidence of such behavior has been documented across almost all domains of DSE, including ridesharing, lodging, and freelancing. However, little is known about the underlying design decisions of DSE platforms which allow certain demographics of the market to gain unfair advantage over others. To bridge this knowledge gap, in this paper, we systematically synthesize evidence from 58 interdisciplinary studies to identify the pervasive discrimination concerns affecting DSE platforms along with their triggering features and mitigation strategies. Our objective is to consolidate such interdisciplinary evidence from a software design point of view. Our results show that existing evidence is mainly geared towards documenting and mitigating issues of racism and sexism affecting platforms of ridesharing, lodging, and freelancing. Our review further shows that discrimination concerns in the DSE market are commonly enabled by features of user profiles and commonly impact reputation systems.

Recent research has exposed a serious discrimination problem affecting applications of the Digital Sharing Economy (DSE), such as Uber, Airbnb, and TaskRabbit. To control for this problem, several DSE apps have crafted a new form of usage policies, known as non-discrimination policies (NDPs). These policies are intended to outline end-users' rights of equal treatment and describe how acts of bias and discrimination over DSE apps are identified and prevented. However, there is still a major knowledge gap in how such non-code artifacts can be formulated, structured, and evolved. To bridge this gap, in this paper, we introduce a first-of-its-kind framework for analyzing and evaluating the content of NDPs in the DSE market. Our analysis is conducted using a dataset of 108 DSE apps, sampled from a broad range of application domains. Our results show that, a) most DSE apps do not provide a separate NDP, b) the majority of existing policies are either extremely brief or combined as sub-statements of other usage policies, and c) most apps do not provide a clear statement of how their NDPs are enforced. Our analysis in this paper is intended to assist DSE app developers with drafting and evolving more comprehensive NDPs as well as help end-users of these apps to make more informed socioeconomic decisions in one of the fastest growing software ecosystems in the world.

Information Retrieval (IR) methods have been recently employed to provide automatic support for bug localization tasks. However, for an IR-based bug localization tool to be useful, it has to achieve adequate retrieval accuracy. Lower precision and recall can leave developers with large amounts of incorrect information to wade through. To address this issue, in this paper, we systematically investigate the impact of combining various IR methods on the retrieval accuracy of bug localization engines. The main assumption is that different IR methods, targeting different dimensions of similarity between artifacts, can be used to enhance the confidence in each others' results. Five benchmark systems from different application domains are used to conduct our analysis. The results show that a) near-optimal global configurations can be determined for different combinations of IR methods, b) optimized IR-hybrids can significantly outperform individual methods as well as other unoptimized methods, and c) hybrid methods achieve their best performance when utilizing information-theoretic IR methods. Our findings can be used to enhance the practicality of IR-based bug localization tools and minimize the cognitive overload developers often face when locating bugs.

Open Source Software (OSS) projects start with an initial vocabulary, often determined by the first generation of developers. This vocabulary, embedded in code identifier names and internal code comments, goes through multiple rounds of change, influenced by the interrelated patterns of human (e.g., developers joining and departing) and system (e.g., maintenance activities) interactions. Capturing the dynamics of this change is crucial for understanding and synthesizing code changes over time. However, existing code evolution analysis tools, available in modern version control systems such as GitHub and SourceForge, often overlook the linguistic aspects of code evolution. To bridge this gap, in this paper, we propose to study code evolution in OSS projects through the lens of developers' language, also known as code lexicon. Our analysis is conducted using 32 OSS projects sampled from a broad range of application domains. Our results show that different maintenance activities impact code lexicon differently. These insights lay out a preliminary foundation for modeling the linguistic history of OSS projects. In the long run, this foundation will be utilized to provide support for basic program comprehension tasks and help researchers gain new insights into the complex interplay between linguistic change and various system and human aspects of OSS development.

Recent evidence has revealed that Sharing Economy platforms such as Uber, Airbnb, and TaskRabbit, have become active hubs for digital discrimination. This new form of discrimination refers to a phenomenon where a business transaction is influenced by race, gender, age, or any other non-business related characteristic of providers or consumers. Existing research often tackles this problem from a socio-economic and regulatory points of view. However, the research on the design aspects of Sharing Economy software, which enable such complex sociotechnical problems to emerge online, is still underdeveloped. To bridge this gap, in this paper, we propose a new perspective on digital discrimination, tackling the problem from a Requirements Engineering point of view. Specifically, we analyze a large dataset of online user feedback as well as synthesize existing literature to identify and classify pervasive discrimination concerns in the Sharing Economy market. Based on this analysis, we devise a crowd-driven domain model to represent these concerns along with their relations to the functional features and user goals of Sharing Economy platforms. This model is intended to provide requirements engineers, working on Sharing Economy software, with systematic insights into the complex types of socio-technical problems that can emerge in the operational environments of their systems.

In this paper, we seek to advance the state-of-the-art in code evolution analysis research and practice by statistically analyzing, interpreting, and formally describing the evolution of code lexicon in Open Source Software (OSS). The underlying hypothesis is that, similar to natural language, code lexicon falls under the remit of evolutionary principles. Therefore, adapting theories and statistical models of natural language evolution to code is expected to provide unique insights into software evolution. Our analysis in this paper is conducted using 2,000 OSS systems sampled from a broad range of application domains. Our results show that a) OSS projects exhibit a significant shift in their linguistic identity over time, b) different syntactic structures of code lexicon evolve differently, c) different factors of OSS development and different maintenance activities impact code lexicon differently. These insights lay out a preliminary foundation for modeling the linguistic history of OSS projects. In the long run, this foundation will be utilized to provide support for basic software maintenance and program comprehension activities, and gain new theoretical insights into the complex interplay between linguistic change and various system and human aspects of OSS development.