A Survey of App Store Analysis for Software Engineering

Martin, William H.; Sarro, Federica; Jia, Yunyi; Zhang, Yuanyuan; Harman, Mark

doi:10.1109/tse.2016.2630689

Cited by 338 publications

(258 citation statements)

References 211 publications

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“…Among the most studied platforms for obtaining user feedback are app stores. Martin et al [11] survey the most relevant work in the area. We focus on literature performing the same steps as our approach: classification, grouping and ranking.…”

Section: Related Workmentioning

confidence: 99%

“…Additionally, we compare against three additional weighting schemes: W survey m W survey m W survey m , which uses the W survey weighting scheme but has manually assigned labels for the category attribute-instead of the ones automatically predicted by the classifier, used by the rest of the weighting schemes in this experiment, W no social W no social W no social where all social attributes (i.e., retweets, likes and social rank) are eliminated and emphasis is given on the following attributes: category (w 1 =0.50), duplicates (w 6 =0.33) and sentiment (w 4 =0.16), while w 2 = w 3 = w 5 = 0 and W no mentions W no mentions W no mentions which also uses the W survey scheme but which only ranks tweets that are not mentions 11 . We ran the implementation of the ranking function with the four weight variations on all of the tweets in our truthset.…”

Section: Rankingmentioning

confidence: 99%

See 1 more Smart Citation

A Little Bird Told Me: Mining Tweets for Requirements and Software Evolution

Guzmán

Ibrahim

Glinz

2017

2017 IEEE 25th International Requirements Engineering Conference (RE)

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Abstract-Twitter is one of the most popular social networks. Previous research found that users employ Twitter to communicate about software applications via short messages, commonly referred to as tweets, and that these tweets can be useful for requirements engineering and software evolution. However, due to their large number-in the range of thousands per day for popular applications-a manual analysis is unfeasible.In this work we present ALERTme, an approach to automatically classify, group and rank tweets about software applications. We apply machine learning techniques for automatically classifying tweets requesting improvements, topic modeling for grouping semantically related tweets and a weighted function for ranking tweets according to specific attributes, such as content category, sentiment and number of retweets. We ran our approach on 68,108 collected tweets from three software applications and compared its results against software practitioners' judgement. Our results show that ALERTme is an effective approach for filtering, summarizing and ranking tweets about software applications. ALERTme enables the exploitation of Twitter as a feedback channel for information relevant to software evolution, including end-user requirements.

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Section: Related Workmentioning

confidence: 99%

Section: Rankingmentioning

confidence: 99%

A Little Bird Told Me: Mining Tweets for Requirements and Software Evolution

Guzmán

Ibrahim

Glinz

2017

2017 IEEE 25th International Requirements Engineering Conference (RE)

View full text Add to dashboard Cite

show abstract

“…For example, Gorla et al [6] used API calls to understand how anomalous API calls can highlight aberrant or otherwise suspicious behaviour, while Syer et al [25] used API calls to understand the relationship between defects and platform dependence. A comprehensive survey of App Store Analysis for Software Engineering can be found in the authors' recent survey [22].…”

Section: App Store Analysismentioning

confidence: 99%

Mobile app and app store analysis, testing and optimisation

Harman

Al-Subaihin

Jia

et al. 2016

Proceedings of the International Conference on Mobile Software Engineering and Systems

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“…Martin et al [24] conduct a survey in app store analysis in which they describe and compare studies that focus on analyzing star ratings and user reviews. Harman et al mined 32,108 BlackBerry apps and found a strong correlation between the average star rating of an app and the number of downloads [11].…”

Section: Analyzing Star Ratings and Reviews Of Appsmentioning

confidence: 99%

Studying the consistency of star ratings and the complaints in 1 & 2-star user reviews for top free cross-platform Android and iOS apps

Bezemer

Hassan

2016

Preprint

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How users rate a mobile app via star ratings and user reviews is of utmost importance for the success of an app. Recent studies and surveys show that users rely heavily on star ratings and user reviews that are provided by other users, for deciding which app to download. However, understanding star ratings and user reviews is a complicated matter, since they are influenced by many factors such as the actual quality of the app and how the user perceives such quality relative to their expectations, which are in turn influenced by their prior experiences and expectations relative to other apps on the platform (e.g., iOS versus Android). Nevertheless, star ratings and user reviews provide developers with valuable information for improving the software quality of their app. In an effort to expand their revenue and reach more users, app developers commonly build cross-platform apps, i.e., apps that are available on multiple platforms. As star ratings and user reviews are of such importance in the mobile app industry, it is essential for developers of cross-platform apps to maintain a consistent level of star ratings and user reviews for their apps across the various platforms on which they are available. In this paper, we investigate whether cross-platform apps achieve a consistent level of star ratings and user reviews. We manually identify 19 cross-platform apps and conduct an empirical study on their star ratings and user reviews. By manually tagging 9,902 1 & 2-star reviews of the studied cross-platform apps, we discover that the distribution of the frequency of complaint types varies across platforms. Finally, we study the negative impact ratio of complaint types and find that for some apps, users have higher expectations on one platform. All our proposed techniques and our methodologies are generic and can be used for any app. Our findings show that at least 68% of the studied cross-platform apps do not have consistent star ratings, which suggests that different quality assurance efforts need to be considered by Abstract How users rate a mobile app via star ratings and user reviews is of utmost importance for the success of an app. Recent studies and surveys show that users rely heavily on star ratings and user reviews that are provided by other users, for deciding which app to download. However, understanding star ratings and user reviews is a complicated matter, since they are influenced by many factors such as the actual quality of the app and how the user perceives such quality relative to their expectations, which are in turn influenced by their prior experiences and expectations relative to other apps on the platform (e.g., iOS versus Android). Nevertheless, star ratings and user reviews provide developers with valuable information for improving the software quality of their app.In an effort to expand their revenue and reach more users, app developers commonly build cross-platform apps, i.e., apps that are available on multiple platforms. As star ratings and user reviews are of such importance in the mobil...

show abstract

A Survey of App Store Analysis for Software Engineering

Cited by 338 publications

References 211 publications

A Little Bird Told Me: Mining Tweets for Requirements and Software Evolution

A Little Bird Told Me: Mining Tweets for Requirements and Software Evolution

Mobile app and app store analysis, testing and optimisation

Studying the consistency of star ratings and the complaints in 1 & 2-star user reviews for top free cross-platform Android and iOS apps

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