Enhancing users\u27 experiences with mobile app stores: What do users see? What should they see?

Using mobile applications is one of the daily habits for most smartphone users. In order to select applications, individuals need to explore the apps stores. Apps’ exploration is disturbed by the way of illustrating the applications’ information. This dissertation consists of three studies that aimed to: 1) Investigate the users’ experience with the apps’ stores; 2) Collect the users’ needs and requirements in order to have a better experience with the interface of apps’ stores; 3) Propose and evaluate a new interface design for the apps’ stores. Different types of data collection methods were administered while proceeding with the phases of this dissertation. The first study was an exploratory study, which administered an online survey, where we had102 respondents. The second study, aimed to collect the design requirements, and we interviewed 16 individuals. The third study was the interface evaluation, where we also had 35 participants. Our results showed multiple factors that affect users’ experience while discovering applications on the apps’ store. Our findings suggested that the current interface design of apps’ stores needs revisions to help users to be aware of apps’ emerging features and issues. Moreover, we found that visual cues that illustrate apps’ information would be more effective to help users perceive specific information about apps. Furthermore, visual indicators would enhance users’ knowledge regarding some of the apps’ concerns. At the end of this research, we evaluated a proposed interface design that integrates the previous design recommendations. The evaluation results illustrated positive outputs in terms of users’ satisfaction and task-completion rate. The findings indicated that participants were delighted to experience the new way of interaction with the interface of apps’ store. We anticipate that users’ experience and their awareness towards the apps issue would be improved if apps’ stores considered adopting the proposed design concept

iOS Applications Testing

Mobile applications conquer the world, but iOS devices hold the major share of tablets market among the corporate workers. This study aims to identify the aspects (i.e. features and/ or limitations) that influence the testing of the native iOS applications. The aspects related to general mobile applications testing are identified through the systematic literature review of academic sources. iOS applications testing aspects are identified through the review of non-academic (multivocal) literature sources. The identified aspects are merged and discussed in detail using the reviewed sources and based on the author’s professional experience in iOS applications testing. The references to the credible sources are provided in order to support the professional experience findings. The study eliminates the gap that exists in the academic world in regards to iOS applications testing. The practitioners are also encouraged to fulfill their iOS applications testing strategies with the identified aspects

Detection of spam review on mobile app stores, evaluation of helpfulness of user reviews and extraction of quality aspects using machine learning techniques

As mobile devices have overtaken fixed Internet access, mobile applications and distribution platforms have gained in importance. App stores enable users to search and purchase mobile applications and then to give feedback in the form of reviews and ratings. A review might contain critical information about user experience, feature requests and bug reports. User reviews are valuable not only to developers and software organizations interested in learning the opinion of their customers but also to prospective users who would like to find out what others think about an app. Even though some surveys have inventoried techniques and methods in opinion mining and sentiment analysis, no systematic literature review (SLR) study had yet reported on mobile app store opinion mining and spam review detection problems. Mining opinions from app store reviews requires pre-processing at the text and content levels, including filtering-out nonopinionated content and evaluating trustworthiness and genuineness of the reviews. In addition, the relevance of the extracted features are not cross-validated with main software engineering concepts. This research project first conducted a systematic literature review (SLR) on the evaluation of mobile app store opinion mining studies. Next, to fill the identified gaps in the literature, we used a novel convolutional neural network to learn document representation for deceptive spam review detection by characterizing an app store review dataset which includes truthful and spam reviews for the first time in the literature. Our experiments reported that our neural network based method achieved 82.5% accuracy, while a baseline Support Vector Machine (SVM) classification model reached only 70% accuracy despite leveraging various feature combinations. We next compared four classification models to assess app store user review helpfulness and proposed a predictive model which makes use of review meta-data along with structural and lexical features for helpfulness prediction. In the last part of this research study, we constructed an annotated app store review dataset for the aspect extraction task, based on ISO 25010 - Systems and software Product Quality Requirements and Evaluation standard and two deep neural network models: Bi-directional Long-Short Term Memory and Conditional Random Field (Bi-LSTM+CRF) and Deep Convolutional Neural Networks and Conditional Random Field (CNN+CRF) for aspect extraction from app store user reviews. Both models achieved nearly 80% F1 score (the weighted average of precision and recall which takes both false positives and false negatives into account) in exact aspect matching and 86% F1 score in partial aspect matching

Techniques for Efficient and Effective Mobile Testing

The booming mobile app market attracts a large number of developers. As a result, the competition is extremely tough. This fierce competition leads to high standards required for mobile apps, which mandates efficient and effective testing. Efficient testing requires little effort to use, while effective testing checks that the app under test behaves as expected. Manual testing is highly effective, but it is costly. Automatic testing should come to the rescue, but current automatic methods are either ineffective or inefficient. Methods using implicit specifications – for instance, “an app should not crash” for catching fail-stop errors – are ineffective because they cannot find semantic problems. Methods using explicit specifications such as test scripts are inefficient because they require huge developer effort to create and maintain specifications. In this thesis, we present our two approaches for solving these challenges. We first built the AppDoctor system which efficiently tests mobile apps. It quickly explores an app then slowly but accurately verifies the potential problems to identify bugs without introducing false positives. It uses dependencies discovered between actions to simplify its reports. Our second approach, implemented in the AppFlow system, leverages the ample opportunity of reusing test cases between apps to gain efficiency without losing effectiveness. It allows common UI elements to be used in test scripts then recognizes these UI elements in real apps using a machine learning approach. The system also allows tests to be specified in reusable pieces, and provides a system to synthesize complete test cases from these reusable pieces. It enables robust tests to be created and reused across apps in the same category. The combination of these two approaches enables a developer to quickly test an app on a great number of combinations of actions for fail-stop problems, and effortlessly and efficiently test the app on most common scenarios for semantic problems. This combination covers most of her test requirements and greatly reduces her burden in testing the app

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