App store mining and analysis: MSR for app stores

By Mark Harman, Yue Jia, Yuanyuan Zhang. IEEE Working Conference on Mining Software Repositories, 2012

378 citations recorded by the source corpus.

Subfields: Computer Science Applications, Information Systems

View Paper: https://www.semanticscholar.org/paper/a9c75acb634f9bd831e3d1ad088fd2f6ad47ef06

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Science Applications

1. Walid Maalej
2. Hadeer Nabil
3. Emitzá Guzmán
4. Andreas Zeller
5. Yuanyuan Zhang
6. Emad Shihab
7. Yue Jia
8. Mark Harman

In Information Systems

1. Walid Maalej
2. Yue Jia
3. Mark Harman
4. Emitzá Guzmán
5. Yuanyuan Zhang
6. Hadeer Nabil
7. Ke Mao
8. Ahmed E. Hassan

Citations in the corpus

• User feedback in the appstore: An empirical study (2013, background): “[6] mined the Blackberry app store for technical, customer, and business aspects of applications.”
• A survey of the use of crowdsourcing in software engineering (2017, background): “The growth in the use of app stores as a platform for software deployment and review (Harman et al., 2012; Pagano and Maalej, 2013; Guzman and Maalej, 2014; Chen et al., 2014), is already providing a kind of Hybrid Crowdsourced 1835 Software Engineering.”
• How Do Users Like This Feature? A Fine Grained Sentiment Analysis of App Reviews (2014, methodology): “[6] analyzed technical and business aspects of apps by extracting app features from the official app descriptions using a collocation and a greedy algorithm for the extraction and grouping of features.”
• Checking app behavior against app descriptions (2014, background): “The very idea of app store mining was introduced one year earlier when Harman et al. mined the Blackberry app store [9].”
• How can i improve my app? Classifying user reviews for software maintenance and evolution (2015, background): “[23] introduced app store mining and analyzed technical and business aspects of apps by extracting app”
• A Survey of App Store Analysis for Software Engineering (2017, methodology): “In order to investigate app feature migratory and non-migratory behaviours in current app stores, they mined features from app descriptions using the techniques in the earlier work [92], and used the proposed theory to empirically analyse the migratory and non-migratory behaviours of 4,053 non-free ”
• What Do Mobile App Users Complain About? (2015, background): “Such reviews, like product-reviews in online web stores, are highly correlated with download counts (i.e., purchases) and are a key measure of the app’s success [3], [4].”
• WHYPER: Towards Automating Risk Assessment of Mobile Applications (2013, background): “Harman et al. [42], first used mining approaches on the application description, pricing, and customer ratings in Blackberry App Store.”
• Retrieving and analyzing mobile apps feature requests from online reviews (2013, background): “Previous work has focused on extracting feature requests information from the text of app store descriptions [4] and understanding software reuse in the Android mobile app market along reuse by inheritance and class reuse [5].”
• AR-miner: mining informative reviews for developers from mobile app marketplace (2014, neutral): “In [24], Harman et al.”
• Bug report, feature request, or simply praise? On automatically classifying app reviews (2015, methodology): “[14] extracted app features from the official app description pages using a collocation and a greedy algorithm.”