Aspect/Feature-based Evaluation of Competing Apps

Tarkvara kvaliteedi mõõtmine on väljakutse paljudele ettevõtetele. Kasutaja kogemuse kogumine ja struktureerimine on väga keeruline. Kasutajate tagasiside on tavaliselt liiga üldine ning tarkvara probleemide leidmine ja seostamine tarkvara võimalustega (nt. kas tegemist on turvaveaga või kasutajaliidese probleemiga) on muutumas keeruliseks. Samal ajal näevad kliendid vaeva rakenduste võimalusepõhise võrdlemisega. Klientidel ei ole ühtegi viisi tuvastamaks, milline võimekus töötab hästi eri rakendustes ja milline mitte.\n\rEttevõtted üritavad sundida kliente andma „struktureeritud“ tagasisidet, kuid tagasiside vorme täidetakse tihti pealiskaudselt või eiratakse täielikult. Kuna võrgumüük on tänapäeval tüüpiline tarkvararakenduste evituskanal, hoitakse enamikku kasutajate tagasisidest võrgus ja avalikult kättesaadavalt veebis (nt. Google Play, Apple Store mobiilitarkvara puhul). Sellegipoolest on automaatne väärtusliku info eraldamine, positiivsete ning negatiivsete arvamuste eristamine ning rakenduste klassifitseerimine võimaluste rühmade järgi keeruline. Konkureerivate rakenduste võimalustepõhine võrdlemine on jätkuvalt raske ülesanne. Üks probleemidest on kommentaaride suur arv, mis teeb arvustajate hoiakute jälgimise keeruliseks. Analoogiliselt on keeruline leida koondarvamust tarkvara iga aspekti (või ka „võimaluse“) kohta. Sel põhjusel on hoiakuteanalüüs muutumas aina populaarsemaks. Selles valdkonnas on teostatud palju uurimusi ning loodud ja rakendatud on mitmeid meetodeid ja tööriistu. Võttes aluseks rakenduse ülevaadetest eraldatud informatsiooni, püstitatakse antud töös kolm eesmärki:\n\r1. Tuvastada etteantud rakenduste võimalused.\n\r2. Tuvastada need rakendused, mida võib funktsionaalsuse (nt. võimaluste) poolest pidada konkureerivateks.\n\r3. Võrrelda neid rakendusi kasutades hoiakute analüüsi.Measurement of software quality is a challenge for many companies. It is very complicated to extract and structure experience that users had. The feedback is usually too general, and it is becoming tough to figure out which problems a piece of software has and in which specific features (e.g. security problem, UI). At the same time, customers are struggling with the problem of comparing applications based on their features. There is no way for customers to know exactly which functionality works well, and which does not, in different applications.\n\rCompanies are trying to make customers provide "structured" feedback. However, feedback forms are often filled in superficially and partly or completely ignored. Since selling online is nowadays the typical delivery channel of software applications, most of the customer reviews are stored online and thus publicly available on the web (e.g. Google Play, Apple Store – for mobile software). However, automatically extracting valuable information and separating positive from negative opinions, as well as classifying software apps by feature groups is difficult. Comparing competing applications based on features they have is still a hard problem. One of the problems is the large amount of comments, which makes it difficult to keep track of the reviewers’ variety of sentiments. Likewise, it is hard to figure out a summarized opinion about each aspect (also, widely used the word “feature”) of the software. That is why approaches to sentiment analysis are becoming more and more popular. Much research has been done in this field, and various methods and tools have been developed and applied. Based on information extracted from app reviews, in this thesis, I tackle three goals:\n\r1. For a given app, identify features that this software application has.\n\r2. Identify those applications that can be considered competing apps with regards to functionality (i.e., the set of features provided).\n\r3. Compare these applications using sentiment analysis

DL-Droid: Deep learning based android malware detection using real devices

open access articleThe Android operating system has been the most popular for smartphones and tablets since 2012. This popularity has led to a rapid raise of Android malware in recent years. The sophistication of Android malware obfuscation and detection avoidance methods have significantly improved, making many traditional malware detection methods obsolete. In this paper, we propose DL-Droid, a deep learning system to detect malicious Android applications through dynamic analysis using stateful input generation. Experiments performed with over 30,000 applications (benign and malware) on real devices are presented. Furthermore, experiments were also conducted to compare the detection performance and code coverage of the stateful input generation method with the commonly used stateless approach using the deep learning system. Our study reveals that DL-Droid can achieve up to 97.8% detection rate (with dynamic features only) and 99.6% detection rate (with dynamic + static features) respectively which outperforms traditional machine learning techniques. Furthermore, the results highlight the significance of enhanced input generation for dynamic analysis as DL-Droid with the state-based input generation is shown to outperform the existing state-of-the-art approaches

Are HIV smartphone apps and online interventions fit for purpose?

Sexual health is an under-explored area of Human-Computer Interaction (HCI), particularly sexually transmitted infections such as HIV. Due to the stigma associated with these infections, people are often motivated to seek information online. With the rise of smartphone and web apps, there is enormous potential for technology to provide easily accessible information and resources. However, using online information raises important concerns about the trustworthiness of these resources and whether they are fit for purpose. We conducted a review of smartphone and web apps to investigate the landscape of currently available online apps and whether they meet the diverse needs of people seeking information on HIV online. Our functionality review revealed that existing technology interventions have a one-size-fits-all approach and do not support the breadth and complexity of HIV-related support needs. We argue that technology-based interventions need to signpost their offering and provide tailored support for different stages of HIV, including prevention, testing, diagnosis and management

Disparity between the Programmatic Views and the User Perceptions of Mobile Apps

User perception in any mobile-app ecosystem, is represented as user ratings of apps. Unfortunately, the user ratings are often biased and do not reflect the actual usability of an app. To address the challenges associated with selection and ranking of apps, we need to use a comprehensive and holistic view about the behavior of an app. In this paper, we present and evaluate Trust based Rating and Ranking (TRR) approach. It relies solely on an apps' internal view that uses programmatic artifacts. We compute a trust tuple (Belief, Disbelief, Uncertainty - B, D, U) for each app based on the internal view and use it to rank the order apps offering similar functionality. Apps used for empirically evaluating the TRR approach are collected from the Google Play Store. Our experiments compare the TRR ranking with the user review-based ranking present in the Google Play Store. Although, there are disparities between the two rankings, a slightly deeper investigation indicates an underlying similarity between the two alternatives