Analysing app reviews for software engineering: a systematic literature review

Elaine H. T. de Oliveira T. Conte W. Nakamura + 1 lainnya

16 Oktober 2023

The mobile app market has grown over the last decades. With the rise of app stores, users can easily choose an app from thousands, making them less tolerant of low-quality apps. More than ever, users are looking for apps that provide not only valuable functionalities but pleasurable experiences. Hence, User eXperience (UX) became the differential to stand out from competitors. By understanding what factors affect UX, practitioners could focus on factors that lead to positive UX while mitigating those that affect UX negatively. In this context, reviews from app stores emerged as a valuable source of information to investigate such factors. However, analyzing millions of reviews is costly and time-consuming. This paper presents UX-MAPPER, an approach to analyzing app store reviews and supporting practitioners in identifying factors affecting UX. We applied the Design Science Research method to design UX-MAPPER iteratively and grounded on a solid theoretical background. We performed exploratory studies to investigate the problem, a systematic mapping study to identify factors that affect UX, and an empirical study with 14 participants with experience in requirements engineering to determine the relevance and acceptance of our proposal from practitioners’ perspectives. The participants considered it useful to improve the quality of existing apps and explore the reviews of competing apps to identify functionalities and features that users are requesting, liking, or hating. They were also willing to use it when it became available, highlighting our proposal’s usefulness and relevance in software development.

Nathan Cassee G. Bavota Nicole Novielli + 3 lainnya

7 Maret 2022

Opinion mining, sometimes referred to as sentiment analysis, has gained increasing attention in software engineering (SE) studies. SE researchers have applied opinion mining techniques in various contexts, such as identifying developers’ emotions expressed in code comments and extracting users’ critics toward mobile apps. Given the large amount of relevant studies available, it can take considerable time for researchers and developers to figure out which approaches they can adopt in their own studies and what perils these approaches entail. We conducted a systematic literature review involving 185 papers. More specifically, we present (1) well-defined categories of opinion mining-related software development activities, (2) available opinion mining approaches, whether they are evaluated when adopted in other studies, and how their performance is compared, (3) available datasets for performance evaluation and tool customization, and (4) concerns or limitations SE researchers might need to take into account when applying/customizing these opinion mining techniques. The results of our study serve as references to choose suitable opinion mining tools for software development activities and provide critical insights for the further development of opinion mining techniques in the SE domain.

Wenhan Zhu Michael W. Godfrey Li Li + 3 lainnya

2 Januari 2024

“App stores” are online software stores where end users may browse, purchase, download, and install software applications. By far, the best known app stores are associated with mobile platforms, such as Google Play for Android and Apple’s App Store for iOS. The ubiquity of smartphones has led to mobile app stores becoming a touchstone experience of modern living. App stores have been the subject of many empirical studies. However, most of this research has concentrated on properties of the apps rather than the stores themselves. Today, there is a rich diversity of app stores and these stores have largely been overlooked by researchers: app stores exist on many distinctive platforms, are aimed at different classes of users, and have different end-goals beyond simply selling a standalone app to a smartphone user. The goal of this paper is to survey and characterize the broader dimensionality of app stores, and to explore how and why they influence software development practices, such as system design and release management. We begin by collecting a set of app store examples from web search queries. By analyzing and curating the results, we derive a set of features common to app stores. We then build a dimensional model of app stores based on these features, and we fit each app store from our web search result set into this model. Next, we performed unsupervised clustering to the app stores to find their natural groupings. Our results suggest that app stores have become an essential stakeholder in modern software development. They control the distribution channel to end users and ensure that the applications are of suitable quality; in turn, this leads to developers adhering to various store guidelines when creating their applications. However, we found the app stores operational model could vary widely between stores, and this variability could in turn affect the generalizability of existing understanding of app stores.

P. T. Nguyen Claudio Di Sipio Massimiliano Di Penta + 2 lainnya

15 Februari 2021

Software development activity has reached a high degree of complexity, guided by the heterogeneity of the components, data sources, and tasks. The proliferation of open-source software (OSS) repositories has stressed the need to reuse available software artifacts efficiently. To this aim, it is necessary to explore approaches to mine data from software repositories and leverage it to produce helpful recommendations. We designed and implemented FOCUS as a novel approach to provide developers with API calls and source code while they are programming. The system works on the basis of a context-aware collaborative filtering technique to extract API usages from OSS projects. In this work, we show the suitability of FOCUS for Android programming by evaluating it on a dataset of 2,600 mobile apps. The empirical evaluation results show that our approach outperforms two state-of-the-art API recommenders, UP-Miner and PAM, in terms of prediction accuracy. We also point out that there is no significant relationship between the categories for apps defined in Google Play and their API usages. Finally, we show that participants of a user study positively perceive the API and source code recommended by FOCUS as relevant to the current development context.

Alberto Dumont Alves Oliveira D. M. Eler Wajdi Aljedaani + 3 lainnya

19 April 2023

Accessibility reviews collected from app stores may contain valuable information for improving apps accessibility. Recent studies have presented insightful information on accessibility reviews, but they were based on small datasets and focused on general accessibility concerns. In this paper, we analyzed accessibility reviews that report issues affecting users with visual disabilities or conditions. Such reviews were identified based on selection criteria applied over 179,519,598 reviews of popular apps on the Google Play Store. Our results show that only 0,003% of user reviews mention visual disabilities or conditions; accessibility reviews are associated with 36 visual disabilities or eye conditions; many users do not give precise feedback and refer to their disability using generic terms; accessibility reviews can be grouped into general topics of concerns related to different types of disabilities; and positive reviews are generally associated with high scores and negative feedback with lower scores.