https://onlinelibrary.wiley.com/doi/pdf/10.1002/stvr.247

Problem frames: analyzing and structuring software development problems. By Michael Jackson. Published by Addison‐Wesley, Boston, Massachusetts, U.S.A., ACM Press Series, 2001. ISBN: 0‐201‐59627‐X, 390 pages. Price: U.K. ?32.95, U.S.A. $49.99, soft cover.

App stores allow users to submit feedback for downloaded apps in form of star ratings and text reviews. Recent studies analyzed this feedback and found that it includes information useful for app developers, such as user requirements, ideas for improvements, user sentiments about specific features, and descriptions of experiences with these features. However, for many apps, the amount of reviews is too large to be processed manually and their quality varies largely. The star ratings are given to the whole app and developers do not have a mean to analyze the feedback for the single features. In this paper we propose an automated approach that helps developers filter, aggregate, and analyze user reviews. We use natural language processing techniques to identify fine-grained app features in the reviews. We then extract the user sentiments about the identified features and give them a general score across all reviews. Finally, we use topic modeling techniques to group fine-grained features into more meaningful high-level features. We evaluated our approach with 7 apps from the Apple App Store and Google Play Store and compared its results with a manually, peer-conducted analysis of the reviews. On average, our approach has a precision of 0.59 and a recall of 0.51. The extracted features were coherent and relevant to requirements evolution tasks. Our approach can help app developers to systematically analyze user opinions about single features and filter irrelevant reviews.

/pdf/how-do-users-like-this-feature-a-fine-grained-sentiment-4el59000r2.pdf

How Do Users Like This Feature? A Fine Grained Sentiment Analysis of App Reviews

With the popularity of smartphones and mobile devices, mobile application (a.k.a. “app”) markets have been growing exponentially in terms of number of users and downloads. App developers spend considerable effort on collecting and exploiting user feedback to improve user satisfaction, but suffer from the absence of effective user review analytics tools. To facilitate mobile app developers discover the most “informative” user reviews from a large and rapidly increasing pool of user reviews, we present “AR-Miner” — a novel computational framework for App Review Mining, which performs comprehensive analytics from raw user reviews by (i) first extracting informative user reviews by filtering noisy and irrelevant ones, (ii) then grouping the informative reviews automatically using topic modeling, (iii) further prioritizing the informative reviews by an effective review ranking scheme, (iv) and finally presenting the groups of most “informative” reviews via an intuitive visualization approach. We conduct extensive experiments and case studies on four popular Android apps to evaluate AR-Miner, from which the encouraging results indicate that AR-Miner is effective, efficient and promising for app developers.

https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=3323&context=sis_research

AR-miner: mining informative reviews for developers from mobile app marketplace

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.

/pdf/a-survey-of-app-store-analysis-for-software-engineering-4mwd81w21w.pdf

A Survey of App Store Analysis for Software Engineering

App stores like Google Play and Apple AppStore have over 3 Million apps covering nearly every kind of software and service. Billions of users regularly download, use, and review these apps. Recent studies have shown that reviews written by the users represent a rich source of information for the app vendors and the developers, as they include information about bugs, ideas for new features, or documentation of released features. This paper introduces several probabilistic techniques to classify app reviews into four types: bug reports, feature requests, user experiences, and ratings. For this we use review metadata such as the star rating and the tense, as well as, text classification, natural language processing, and sentiment analysis techniques. We conducted a series of experiments to compare the accuracy of the techniques and compared them with simple string matching. We found that metadata alone results in a poor classification accuracy. When combined with natural language processing, the classification precision got between 70–95% while the recall between 80–90%. Multiple binary classifiers outperformed single multiclass classifiers. Our results impact the design of review analytics tools which help app vendors, developers, and users to deal with the large amount of reviews, filter critical reviews, and assign them to the appropriate stakeholders.

https://mobis.informatik.uni-hamburg.de/wp-content/uploads/2015/06/review_classification_preprint.pdf

Bug report, feature request, or simply praise? On automatically classifying app reviews

User feedback is imperative in improving software quality. In this paper, we explore the rich set of user feedback available for third party mobile applications as a way to extract new/changed requirements for next versions. A potential problem using this data is its volume and the time commitment involved in extracting new/changed requirements. Our goal is to alleviate part of the process through automatic topic extraction. We process user comments to extract the main topics mentioned as well as some sentences representative of those topics. This information can be useful for requirements engineers to revise the requirements for next releases. Our approach relies on adapting information retrieval techniques including topic modeling and evaluating them on different publicly available data sets. Results show that the automatically extracted topics match the manually extracted ones, while also significantly decreasing the manual effort.

Analysis of user comments: an approach for software requirements evolution

As the use of computers has grown, so too has concern about the amount of power they consume. Data centers, for example, are limited in scalability as they struggle with soaring energy costs from many large companies relying on fast, reliable, and round-the-clock computing services. On large-scale computing clusters, like data centers, even a small drop in power consumption can have large effects. Across computing contexts, reducing power consumed by computers has become a major focus. In this paper, we present a new approach for mapping software design to power consumption and present empirical results of the approach on different software implementations. In particular, we compare the power profiles of software using design patterns against software not using design patterns as a way to explore how high-level design decisions affect an application's energy usage. We show how mappings between software design and power consumption profiles can provide software designers and developers with useful information about the power behavior of the software they are developing. The goal is for software engineers to use this information in designing and developing more energy efficient solutions.

Initial explorations on design pattern energy usage

As the use of computers has grown, so too has concern about the amount of power that they consume. Data centers, for example, are limited in scalability as they struggle with soaring energy costs as many large companies rely on fast, reliable, and round-the-clock computing services. On large-scale computing clusters, like data centers, even a small drop in power consumption can have large effects. Across computing contexts, reducing power consumed by computers has become a major focus. In this paper, we present a new approach and tool for mapping software design to power consumption and describe how such mappings can provide software designers and developers useful information about the power behavior of the software they are developing. The goal is for software engineers to use this information in designing and developing more energy efficient solutions.

Towards power reduction through improved software design

Software engineers make decisions about the design of the software they are creating on a daily basis. These decisions may impact the application in terms of efficiency, usability, flexibility, etc. Different competing design decisions are therefore often evaluated in terms of their projected impact on quality metrics prior to implementation. Recently energy has become a concern for software systems, ranging from mobile devices to large data centers. Additionally, it has been recognized that the software executing on a computing device can have a significant impact on the device's energy consumption. This raises the obvious question of whether or not it is possible to reduce the energy consumption of a software system by the means of software design decisions. This work examines how the use of different servers impacts the energy consumption of a web application. Through a controlled empirical experiment we have discovered several important findings in this regard. The results indicate that the energy consumption of a web application can vary greatly depending on the web server used to handle its requests. Furthermore, different web servers are more or less energy efficient depending on which web application features are being executed. The paper details an analysis of the results of the experiment.

Investigating the impacts of web servers on web application energy usage

Identifying ambiguous requirements is an important aspect of software development, as it prevents design and implementation errors that are costly to correct. Unfortunately, few efforts have been made to automatically solve the problem. In this paper, we study the problem of lexical ambiguity detection and propose methods that can automatically identify potentially ambiguous concepts in software requirement specifications. Specifically, we focus on two types of lexical ambiguities, i.e., Overloaded and Synonymous ambiguity. Experiment results over four real-world software requirement collections show that the proposed methods are effective in detecting ambiguous terminology.

/pdf/automatic-detection-of-ambiguous-terminology-for-software-40j3gpth09.pdf

Kristina Winbladh

Papers

Analysis of user comments: an approach for software requirements evolution

Initial explorations on design pattern energy usage

Towards power reduction through improved software design

Investigating the impacts of web servers on web application energy usage

Automatic Detection of Ambiguous Terminology for Software Requirements