Too Many User-Reviews! What Should App Developers Look at First?

Noei, Ehsan; Zhang, Feng; Zou, Ying

doi:10.1109/tse.2019.2893171

Cited by 54 publications

(38 citation statements)

References 49 publications

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“…Our proposal can couple the reputation scheme to indicate which security service the developer should concentrate on. Noei et al [10] investigated significant relationship between reviews and increase of star-ratings. They looked for key topics of user-reviews per category that developers should improve to achieve higher star-ratings.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A Security-Related Reputation Scheme of Android Apps Based on NLP Analysis of Comments

Tchakounté¹,

Pagore²,

Atemkeng³

et al. 2020

Preprint

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Comments are exploited by product vendors to measure satisfaction of consumers. With the advent of Natural Language Processing (NLP), comments on Google Play can be processed to extract knowledge on applications such as their reputation. Proposals in that direction are either informal or interested merely on functionality. Unlike, this work aims to determine reputation of Android applications in terms of confidentiality, integrity, availability and authentication (CIAA). This work proposes a model of assessing app reputation relying on sentiment analysis and text analysis of comments. While assuming that comments are reliable, we collect Google Play applications subject to comments which include security keywords. An in-depth analysis of keywords based on Naive Bayes classification is made to provide polarity of any comment. Based on comment polarity, reputation is evaluated for the whole application. Experiments made on real applications including dozens to billions of comments, reveal that developers lack to make efforts to guarantee CIAA services. A fine-grained analysis shows that not security reputed applications can be reputed in specific CIAA services. Results also show that applications with negative security polarities display in general positive functional polarities. This result suggests that security checking should include careful comment analysis to improve security of applications.

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Section: Related Workmentioning

confidence: 99%

“…This proposal is subjective, does not exploit sentiment analysis on feedbacks and is subject to false reputation in case there are few feedbacks. Some authors rely on review analyses to reveal aspects to improve during application updates [9], [10,11]. Their interest focuses on functionality.…”

Section: Introductionmentioning

confidence: 99%

A Security-Related Reputation Scheme of Android Apps Based on NLP Analysis of Comments

Tchakounté¹,

Pagore²,

Atemkeng³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…A recent research 14 demonstrated that each app category has a specific set of key topics (i.e., the topics appearing in app user reviews having statistically significant relationships with star ratings). However, it is still unclear what are the types of issues that are raised in app user reviews having negative impact on the star rating.…”

Section: Introductionmentioning

confidence: 99%

Investigating the criticality of user‐reported issues through their relations with app rating

Sorbo

Grano

Visaggio

et al. 2020

J Software Evolu Process

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App quality impacts user experience and satisfaction. As a consequence, both app ratings and user feedback reported in app reviews are directly influenced by the user-perceived app quality. Through an empirical study involving 210,517 reviews related to 317 Android apps, in this paper, we experiment with the combined usage of app rating and user reviews analysis (i) to investigate the most important factors influencing the perceived app quality, (ii) focusing on the topics discussed in user review that most relate with app rating. Besides, we investigate whether specific code quality metrics could be monitored to prevent the rising of negative user feedback (i.e., types of user review comments), connected with low ratings. Our study demonstrates that user comments reporting bugs are negatively correlated with the rating, while reviews reportingfeature requests do not. Interestingly, depending on the app category, we observed that different kinds of issues have rather different relationships with the rating and the user-perceived quality of the app. In particular, we observe that for specific app categories (e.g., communication), some code quality factors have significant relationships with the raising of certain types of feedback, which, in turn, are negatively connected with app ratings.

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“…This challenge is addressed in a number of ways including energy consumption profiling [3], developing a catalog of energy patterns [4], and investigating users' reviews discussed on app stores [11]. Previous work shows that users discuss the battery related issues (energy inefficiency or energy consumption) of the apps in their reviews [11], [9]. However, there is no work that addresses the automatic extraction of the battery related issues from users' feedback.…”

Section: Introductionmentioning

confidence: 99%

“…Considering the importance of energy efficiency in mobile apps [4] and the fact that it is a main topic discussed by app users [9], we empirically studied 90 different models to extract energy-related issues from app reviews. These models include different machine learning (with various feature combinations) and deep learning models.…”

Section: Introductionmentioning

confidence: 99%

ReviewViz

Hadi

Fard

2020

Proceedings of the IEEE/ACM 7th International Conference on Mobile Software Engineering and Systems

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Improving energy efficiency of mobile applications is a topic that has gained a lot of attention recently. It has been addressed in a number of ways such as identifying energy bugs and developing a catalog of energy patterns. Previous work shows that users discuss the battery related issues (energy inefficiency or energy consumption) of the apps in their reviews. However, there is no work that addresses the automatic extraction of the battery related issues from users' feedback. In this paper, we report on a visualization tool that is developed to empirically study machine learning algorithms and text features to automatically identify the energy consumption specific reviews with the highest accuracy. Other than the common machine learning algorithms, we utilize deep learning models with different word embeddings to compare the results. Furthermore, to help the developers extract the main topics that are discussed in the reviews, two state of the art topic modeling algorithms are applied. The visualizations of the topics represent the keywords that are extracted for each topic along with a comparison with the results of string matching. The developed web-browser based interactive visualization tool is a novel framework developed with the intention of giving the app

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Too Many User-Reviews! What Should App Developers Look at First?

Cited by 54 publications

References 49 publications

A Security-Related Reputation Scheme of Android Apps Based on NLP Analysis of Comments

A Security-Related Reputation Scheme of Android Apps Based on NLP Analysis of Comments

Investigating the criticality of user‐reported issues through their relations with app rating

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