MARAM: Tool Support for Mobile App Review Management

Mobile apps today have millions of user reviews available online. Such reviews cover a large broad of themes and are usually expressed in an informal language. They pro- vide valuable information to developers, such as feature re- quests, bug reports, and detailed descriptions of one’s in- teraction with the app. Due to the overwhelmingly large number of reviews apps usually get associated with, manag- ing and making sense of reviews is difficult. In this paper, we address this problem by introducing MARAM, a tool de- signed to provide support for managing and integrating on- line reviews with other software management tools available, such as GitHub, JIRA and Bugzilla. The tool is designed to a) automatically extract app development relevant infor- mation from online reviews, b) support developers’ queries on (subsets of ) the user generated content available on app stores, namely online reviews, feature requests, and bugs, and c) support the management of online reviews and their integration with other software management tools available, namely GitHub, JIRA or Bugzilla

How Should You plan Your App’s Features? Selecting and Prioritizing A Mobile App’s Initial Features Based on User Reviews

The app market is extremely competitive, with users typically having several alternative app possibilities. To attract and retain users, it is imperative for developers to consider the ratings and reviews their apps receive. App reviews frequently contain feature requests, sometimes hidden among complaints. Developers use these complaints and requests to improve their apps, thus increasing their rating which is incredibly important for attracting new users. Unfortunately, developers of new apps are at a severe disadvantage: They do not have the benefit of existing reviews, with only the reviews of similar apps to potentially rely upon. To address this problem, we conducted a study and developed a novel technique that extracts feature requests from similar, existing apps to help prioritize the features and requirements important in an initial app release. We compared different classification models in order to identify most appropriate classifier for classifying reviews category-wise. We found that there is not one single classifier that could have a higher accuracy than others for all categories.Our study also involved extracting features from user reviews in the Google Play store. The features were presented to 17 Android developers twice; once without applying our technique and once after applying our technique. Our proposed technique created a 48\% reduction in the number of features considered high priority by participants; helping developers focus on what features to consider for their apps. We surprisingly found that the frequency of requested features did not impact the developer\u27s decisions in prioritizing the features in the inclusion of new apps

Opinion-Driven App Recommender System (ODARS)

Hetkel puuduvad tõhusad vahendid saamaks kiiret ülevaadet sellest, mida inimesed arvavad kindlatest rakendustest ja nende võimalustest, või milline rakendus on teistest parem. Rakenduse alla laadimisel tuginevad kasutajad peamiselt rakendusele antud hinnangutele, ülevaadetele ja arvustustele. Paraku on inimese jaoks keeruline lugeda läbi kõik arvustused, et saada rakendusest ülevaade ja mõista, kui positiivselt või negatiivselt on inimesed arvanud kindlatest funktsionaalsustest. Hiljaaegu pakuti rakenduse funktsionaalsuse eraldamiseks kasutajate ülevaadetest ja kirjeldusest välja SAFE lähenemisviis. See lähenemisviis on tõestanud oma ülimuslikkust varasemate tehnikate suhtes. Siiski ei leidu ühtegi tööriista, mis kasutaks hiljutist SAFE lähenemisviisi, et analüüsida kasutajate meelestatust, mida on mainitud kasutajate rakenduse funktsionaalsust puututavates ülevaadetes.Antud uurimuse eesmärgiks on välja arendada tööriist, et analüüsida rakenduse funktsionaalsust puudutavat meelestatust kasutajate ülevaadetes. See tööriist võimaldab analüüsida ühte või mitut rakendust. Rakenduse funktsionaalsused eraldatakse kasutajate ülevaadetest ja kirjeldusest. Väljaarendatud tööriista kasutuslihtsuse, kasulikkuse ja tulevikuväljavaate hindamiseks viiakse läbi küsitlus.Currently, there are no decent ways for the users to quickly determine what people are thinking about the specific application and its features, or which application is better than the other. Users mainly rely on the ratings, some articles or reviews before downloading the application. Unfortunately, it is really difficult for the human to go through all reviews in order to get an impression on an application, to see how positively or negatively people have been thinking about the specific features.Recently, a SAFE approach was proposed for app feature extraction from user reviews and app description. The approach has shown its superiority over the previous techniques. However, there is no tool that that uses the recent SAFE approach to analyze user sentiments mentioned in user reviews at app feature-level.The intention of this study is to develop a tool to analyze user sentiments mentioned on app features in user reviews. The tool enables to perform analysis at single app or multiple apps levels. The app features are extracted from the user reviews and app description together. A survey is conducted to evaluate the developed tool based on its ease of use, usefulness and future use