Mining app reviews to support software engineering

The thesis studies how mining app reviews can support software engineering. App reviews —short user reviews of an app in app stores— provide a potentially rich source of information to help software development teams maintain and evolve their products. Exploiting this information is however difficult due to the large number of reviews and the difficulty in extracting useful actionable information from short informal texts. A variety of app review mining techniques have been proposed to classify reviews and to extract information such as feature requests, bug descriptions, and user sentiments but the usefulness of these techniques in practice is still unknown. Research in this area has grown rapidly, resulting in a large number of scientific publications (at least 182 between 2010 and 2020) but nearly no independent evaluation and description of how diverse techniques fit together to support specific software engineering tasks have been performed so far. The thesis presents a series of contributions to address these limitations. We first report the findings of a systematic literature review in app review mining exposing the breadth and limitations of research in this area. Using findings from the literature review, we then present a reference model that relates features of app review mining tools to specific software engineering tasks supporting requirements engineering, software maintenance and evolution. We then present two additional contributions extending previous evaluations of app review mining techniques. We present a novel independent evaluation of opinion mining techniques using an annotated dataset created for our experiment. Our evaluation finds lower effectiveness than initially reported by the techniques authors. A final part of the thesis, evaluates approaches in searching for app reviews pertinent to a particular feature. The findings show a general purpose search technique is more effective than the state-of-the-art purpose-built app review mining techniques; and suggest their usefulness for requirements elicitation. Overall, the thesis contributes to improving the empirical evaluation of app review mining techniques and their application in software engineering practice. Researchers and developers of future app mining tools will benefit from the novel reference model, detailed experiments designs, and publicly available datasets presented in the thesis

Application of Developers' and Users' Dependent Factors in App Store Optimization

This paper presents an application of developers' and users' dependent factors in the app store optimization. The application is based on two main fields: developers' dependent factors and users' dependent factors. Developers' dependent factors are identified as: developer name, app name, subtitle, genre, short description, long description, content rating, system requirements, page url, last update, what's new and price. Users' dependent factors are identified as: download volume, average rating, rating volume and reviews. The proposed application in its final form is modelled after mining sample data from two leading app stores: Google Play and Apple App Store. Results from analyzing collected data show that developer dependent elements can be better optimized. Names and descriptions of mobile apps are not fully utilized. In Google Play there is one significant correlation between download volume and number of reviews, whereas in App Store there is no significant correlation between factors

apk2vec: Semi-supervised multi-view representation learning for profiling Android applications

Building behavior profiles of Android applications (apps) with holistic, rich and multi-view information (e.g., incorporating several semantic views of an app such as API sequences, system calls, etc.) would help catering downstream analytics tasks such as app categorization, recommendation and malware analysis significantly better. Towards this goal, we design a semi-supervised Representation Learning (RL) framework named apk2vec to automatically generate a compact representation (aka profile/embedding) for a given app. More specifically, apk2vec has the three following unique characteristics which make it an excellent choice for largescale app profiling: (1) it encompasses information from multiple semantic views such as API sequences, permissions, etc., (2) being a semi-supervised embedding technique, it can make use of labels associated with apps (e.g., malware family or app category labels) to build high quality app profiles, and (3) it combines RL and feature hashing which allows it to efficiently build profiles of apps that stream over time (i.e., online learning). The resulting semi-supervised multi-view hash embeddings of apps could then be used for a wide variety of downstream tasks such as the ones mentioned above. Our extensive evaluations with more than 42,000 apps demonstrate that apk2vec's app profiles could significantly outperform state-of-the-art techniques in four app analytics tasks namely, malware detection, familial clustering, app clone detection and app recommendation.Comment: International Conference on Data Mining, 201

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

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

Web Usage Mining to Extract Knowledge for Modelling Users of Taiwan Travel Recommendation Mobile APP

This work presents the design of a web mining system to understand the navigational behavior of passengers in developed Taiwan travel recommendation mobile app that provides four main functions including recommend by location , hot topic , nearby scenic spots information , my favorite and 2650 scenic spots. To understand passenger navigational patterns, log data from actual cases of app were collected and analysed by web mining system. This system analysed 58981 sessions of 1326 users for the month of June, 2014. Sequential profiles for passenger navigational patterns were captured by applying sequence-based representation schemes in association with Markov models and enhanced K-mean clustering algorithms for sequence behavior mining cluster patterns. The navigational cycle, time, function numbers, and the depth and extent (range) of app were statistically analysed. The analysis results can be used improved the passengers\u27 acceptance of app and help generate potential personalization recommendations for achieving an intelligent travel recommendation service

Mining domain knowledge from app descriptions

Domain analysis aims at obtaining knowledge to a particular domain in the early stage of software development. A key challenge in domain analysis is to extract features automatically from related product artifacts. Compared with other kinds of artifacts, high volume of descriptions can be collected from app marketplaces (such as Google Play and Apple Store) easily when developing a new mobile application (App), so it is essential for the success of domain analysis to obtain features and relationship from them using data technologies. In this paper, we propose an approach to mine domain knowledge from App descriptions automatically. In our approach, the information of features in a single app description is firstly extracted and formally described by a Concern-based Description Model (CDM), this process is based on predefined rules of feature extraction and a modified topic modeling method; then the overall knowledge in the domain is identified by classifying, clustering and merging the knowledge in the set of CDMs and topics, and the results are formalized by a Data-based Raw Domain Model (DRDM). Furthermore, we propose a quantified evaluation method for prioritizing the knowledge in DRDM. The proposed approach is validated by a series of experiments

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