User feedback in the app store

Guzmán, Emitzá; Oliveira, Luis; Steiner, Yves; Wagner, Laura C.; Glinz, Martin

doi:10.1145/3183428.3183436

Cited by 36 publications

(23 citation statements)

References 31 publications

(60 reference statements)

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“…In addition, the iOS App store separates reviews by their country of origin, while the Google Play Store separates reviews by languages. Previous studies have suggested that there exist cultural differences in how users from different countries provide app reviews [9,30]. In order to protect our study from such cultural bias, we only studied US-based users' reviews.…”

Section: Threats To Validitymentioning

confidence: 99%

Learning Features that Predict Developer Responses for iOS App Store Reviews

Srisopha

Link

Swami

et al. 2020

Proceedings of the 14th ACM / IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM)

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Background: Which aspects of an iOS App Store user review motivate developers to respond? Numerous studies have been conducted to extract useful information from reviews, but limited effort has been expended to answer this question. Aims: This work aims to investigate the potential of using a machine learning algorithm and the features that can be extracted from user reviews to model developers' response behavior. Through this process, we want to uncover the learned relationship between these features and developer responses. Method: For our prediction, we run a random forest algorithm over the derived features. We then perform a feature importance analysis to understand the relative importance of each individual feature and groups thereof. Results: Through a case study of eight popular apps, we show patterns in developers' response behavior. Our results demonstrate not only that rating and review length are among the most important but also that review posted time, sentiment, and the writing style play an important role in the response prediction. Additionally, the variation in feature importance ranking implies that different app developers use different feature weights when prioritizing responses. Conclusions: Our results may provide guidance for those building review or response prioritization tools and developers wishing to prioritize their responses effectively. CCS CONCEPTS • Information systems → Data mining; Retrieval models and ranking; • Software and its engineering → Software evolution.

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Section: Threats To Validitymentioning

confidence: 99%

Learning Features that Predict Developer Responses for iOS App Store Reviews

Srisopha

Link

Swami

et al. 2020

Proceedings of the 14th ACM / IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM)

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“…This approach permitted to explicit modeling the changes to other requirements models in response to certain term and conditions like requirements failures. Guzman et al [50] used app stores to elicit user requirements. The authors analyzed 2,560 app reviews written by users from eight countries.…”

Section: Related Workmentioning

confidence: 99%

Your Opinions Let us Know: Mining Social Network Sites to Evolve Software Product Lines

Ali¹,

Hwang²,

Hong³

2019

KSII TIIS

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Software product lines (SPLs) are complex software systems by nature due to their common reference architecture and interdependencies. Therefore, any form of evolution can lead to a more complex situation than a single system. On the other hand, software product lines are developed keeping long-term perspectives in mind, which are expected to have a considerable lifespan and a long-term investment. SPL development organizations need to consider software evolution in a systematic way due to their complexity and size. Addressing new user requirements over time is one of the most crucial factors in the successful implementation SPL. Thus, the addition of new requirements or the rapid context change is common in SPL products. To cope with rapid change several researchers have discussed the evolution of software product lines. However, for the evolution of an SPL, the literature did not present a systematic process that would define activities in such a way that would lead to the rapid evolution of software. Our study aims to provide a requirements-driven process that speeds up the requirements engineering process using social network sites in order to achieve rapid software evolution. We used classification, topic modeling, and sentiment extraction to elicit user requirements. Lastly, we conducted a case study on the smartwatch domain to validate our proposed approach. Our results show that users' opinions can contain useful information which can be used by software SPL organizations to evolve their products. Furthermore, our investigation results demonstrate that machine learning algorithms have the capacity to identify relevant information automatically.

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“…Despite the potential influence of cultural differences on user feedback, there are few studies that have investigated this topic. To our best knowledge, only one study exists that has looked at the impact of culture in reviews written in English [13]. However, this work did not examine app reviews written in different languages and only investigated certain cultural dimensions.…”

Section: Introductionmentioning

confidence: 99%

“…In our work, we focus on two cultural dimensions: Individualism and Uncertainty Avoidance. In our previous work [13] on cultural differences on app reviews, we did not investigate this point, as only countries with similar Uncertainty Avoidance scores were selected. Our current work sheds some light on this aspect by focusing on the analysis of countries with different Individualism and Uncertainty Avoidance scores.…”

Section: Introductionmentioning

confidence: 99%

Does Culture Matter? Impact of Individualism and Uncertainty Avoidance on App Reviews

Fischer

Walczuch

Guzmán

2021

2021 IEEE/ACM 43rd International Conference on Software Engineering: Software Engineering in Society (ICSE-SEIS)

Self Cite

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Mobile applications are often used by an international audience and therefore receive a high daily amount of user reviews from various countries. Previous work found evidence that app store reviews contain helpful information for software evolution processes. However, the cultural diversity of the reviews and its consequences on specific user feedback characteristics has only been researched to a limited extent so far. In this paper, we examine the influence of two cultural dimensions, Individualism and Uncertainty Avoidance on user feedback in Apple app store reviews written in different languages. For this purpose, we collected 647,141 reviews from eight countries and written in five languages over a period of six months. We then used manual content analysis and automated processing to examine a sample of 3,120 reviews. The results show that there is a statistically significant influence of Individualism and Uncertainty Avoidance on user feedback characteristics. The results of this study will help researchers and practitioners to reduce algorithm bias caused by less diversified training and test data and to raise awareness of the importance of analyzing diversified user feedback.

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User feedback in the app store

Cited by 36 publications

References 31 publications

Learning Features that Predict Developer Responses for iOS App Store Reviews

Learning Features that Predict Developer Responses for iOS App Store Reviews

Your Opinions Let us Know: Mining Social Network Sites to Evolve Software Product Lines

Does Culture Matter? Impact of Individualism and Uncertainty Avoidance on App Reviews

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