172 research outputs found
Mobile app and app store analysis, testing and optimisation
This talk presents results on analysis and testing of mobile apps and app stores, reviewing the work of the UCL App Analysis Group (UCLappA) on App Store Mining and Analysis. The talk also covers the work of the UCL CREST centre on Genetic Improvement, applicable to app improvement and optimisation
Terms extractions: an approach for requirements reuse
This paper presents a solution to a requirements
reuse problem that utilises natural language processing and
information retrieval technique. We proposed a semi-automated
approach to extract the software features from online software
review to assist the process to reuse natural language
requirements. We have conducted an experiment to compare the
manual feature extraction versus the semi-automated feature
extraction. We used compilations of software review from the
Internet as a source of this extraction process. The extracted
software features are compared against the features obtained
manually by human and the evaluation results obtained in terms
of time, precision, recall, and F-Measure indicate a promising
result
Towards Mobile Twin Peaks for App Development
Requirements of mobile apps are often hard to elicit from massive numbers of users, although it is important for the solution architecture to meet them. Mobile Twin Peaks approach is proposed as a process of developing apps concurrently and iteratively that incorporates bidirectional communications within a mobile app. The communications allow both requirements engineers and software architects to reach a consensus on functionalities and quality constraints and to adapt architectural design decisions appropriately. To recommend architectural design decisions to the developers, we aim to obtain architecture- critical requirements from a set of general apps by combining, for example, analytics, ethnographic study, and information retrieval. We argue that the effectiveness of these techniques could be evaluated by experimental case studies and by engaging with industry partners to perform action research
Examining User-Developer Feedback Loops in the iOS App Store
Application Stores, such as the iTunes App Store, give developers access to their usersâ complaints and requests in the form of app reviews. However, little is known about how developers are responding to app reviews. Without such knowledge developers, users, App Stores, and researchers could build upon wrong foundations. To address this knowledge gap, in this study we focus on feedback loops, which occur when developers address a user concern. To conduct this study we use both supervised and unsupervised methods to automatically analyze a corpus of 1752 different apps from the iTunes App Store consisting of 30,875 release notes and 806,209 app reviews. We found that 18.7% of the apps in our corpus contain instances of feedback loops. In these feedback loops we observed interesting behaviors. For example, (i) feedback loops with feature requests and login issues were twice as likely as general bugs to be fixed by developers, (ii) users who reviewed with an even tone were most likely to have their concerns addressed, and (iii) the star rating of the app reviews did not influence the developers likelihood of completing a feedback loop
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
Recommending software features to designers: From the perspective of users
With lots of public software descriptions emerging in the application market, it is significant to extract common software features from these descriptions and recommend them to new designers. However, existing approaches often recommend features according to their frequencies which reflect designersâ preferences. In order to identify those usersâ favorite features and help design more popular software, this paper proposes to make use of the public data of usersâ ratings and productsâ downloads which reflect usersâ preferences to recommend extracted features. The proposed approach distinguishes usersâ perspective from designersâ perspective and argues that usersâ perspective is better for recommending features because most products are designed for users and expect to be popular among users. Based on the lasso regression to estimate the relationship between the extracted features and the usersâ ratings, it proposes to first distinguish the extracted features to identify those rec- ommendable and undesirable features. By treating each download as a support from users to the product features, it further mines the feature association rules from usersâ perspective for recommending features. By taking the public data on the market of SoftPedia.com for evaluation, our empirical studies indicate that: (1) selecting recommendable features by lasso regression is better than that by feature frequencies in terms of F1 measure; and (2) recommending features based on the feature association rules mined from usersâ perspective is not only feasible but also has competitive performance compared with that based on the rules mined from designsâ perspective in terms of F1 measure
Causal impact analysis for app releases in google play
App developers would like to understand the impact of their own and their competitors' software releases. To address this we introduce Causal Impact Release Analysis for app stores, and our tool, CIRA, that implements this analysis. We mined 38,858 popular Google Play apps, over a period of 12 months. For these apps, we identified 26,339 releases for which there was adequate prior and posterior time series data to facilitate causal impact analysis. We found that 33% of these releases caused a statistically significant change in user ratings. We use our approach to reveal important characteristics that distinguish causal significance in Google Play. To explore the actionability of causal impact analysis, we elicited the opinions of app developers: 56 companies responded, 78% concurred with the causal assessment, of which 33% claimed that their company would consider changing its app release strategy as a result of our findings
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