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

Opinion Mining for Software Development: A Systematic Literature Review

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

Analyzing user reviews of messaging Apps for competitive analysis

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceThe rise of various messaging apps has resulted in intensively fierce competition, and the era of Web 2.0 enables business managers to gain competitive intelligence from user-generated content (UGC). Text-mining UGC for competitive intelligence has been drawing great interest of researchers. However, relevant studies mostly focus on industries such as hospitality and products, and few studies applied such techniques to effectively perform competitive analysis for messaging apps. Here, we conducted a competitive analysis based on topic modeling and sentiment analysis by text-mining 27,479 user reviews of four iOS messaging apps, namely Messenger, WhatsApp, Signal and Telegram. The results show that the performance of topic modeling and sentiment analysis is encouraging, and that a combination of the extracted app aspect-based topics and the adjusted sentiment scores can effectively reveal meaningful competitive insights into user concerns, competitive strengths and weaknesses as well as changes of user sentiments over time. We anticipate that this study will not only advance the existing literature on competitive analysis using text mining techniques for messaging apps but also help existing players and new entrants in the market to sharpen their competitive edge by better understanding their user needs and the industry trends

Digital Discrimination in the Sharing Economy: Evidence, Policy, and Feature Analysis

Applications (apps) 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 under- lying design decisions of DSE systems which allow certain demographics of the market to gain unfair advantage over others. To bridge this knowledge gap, in this dissertation, we investigate the problem of digital discrimination from a software engineering point of view. To develop an in-depth understanding of the problem, we first synthesize existing evidence on digital discrimination from interdisciplinary literature. We then analyze online user feedback, available on social media channels, to assess end-users’ awareness of discrimination issues affecting their DSE apps. We then introduce a novel protocol for drafting and evaluating nondiscrimination policies (NDPs) in the DSE market. Our objective is to assist DSE developers with drafting high quality and less ambiguous NDPs. Finally, we propose and evaluate a modeling framework for representing discrimination concerns affecting popular DSE apps along with their relations (synergies and tradeoffs) to other system features and user goals. Our objective is to visualize such complex domain knowledge using formal notations that software developers can easily understand, communicate, and utilize as an integral part of their app design process. The impact of the proposed research will extend to the entire population of DSE workers, targeting the deep racial and regional disparities in the DSE market and helping people in resource-constrained communities to overcome key barriers to participation and adaptation in one of the fastest growing software ecosystems in the world

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

Software quality assurance in Scrum the need for concrete guidance on SQA strategies in meeting user expectations

Includes abstract.Includes abstract.The purpose of this study is to identify and present the concerns of project stakeholders in relation to Software Quality Assurance (SQA) in a Scrum environment. Guided by the tenets of Classic Grounded Theory Methodology, this exploratory and inductive case study presents a broad range of SQA concepts related to the main concern of “Meeting User Expectations”. In trying to resolve the main concern, the Scrum project stakeholders alluded to lack of “Concrete Guidance” on SQA strategies, tools, and techniques in Scrum. The lack of concrete guidance in Scrum requires a development team to devise “Innovations” which may include “Adopting Practices” from other methodologies and carefully designing the “Process Structure” to accommodate the “Adopted Practices”, ensure “Continuous Improvement” of the process, and provide an environment for “Collaborative Ownership”. In addition to the “Need for Concrete Guidance”, the study reveals two other important concepts necessary for “Meeting User Expectations”: the “Need for Solid User Representation” and the “Need for Dedicated Testing”. While some Agile proponents claim that the Agile SQA practices are adequate on their own, the study reveals a number of challenges that impact on a team’s ability to meet user expectations when there is no dedicated tester in a Scrum environment