Retrieving and analyzing mobile apps feature requests from online reviews

By Claudia Iacob, Rachel Harrison. IEEE Working Conference on Mining Software Repositories, 2013

356 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/af185f391d73e59c5e062de920d456e87c004d87

How researchers see this paper

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

In Artificial Intelligence

1. Mark Harman
2. Yue Jia
3. Yuanyuan Zhang
4. William J. Martin
5. Federica Sarro
6. Emitzá Guzmán
7. Walid Maalej
8. Gerardo Canfora

In Information Systems

1. Yue Jia
2. Mark Harman
3. Yuanyuan Zhang
4. Walid Maalej
5. Hadeer Nabil
6. Emitzá Guzmán
7. Federica Sarro
8. William J. Martin

In Sociology and Political Science

1. Emitzá Guzmán
2. Yue Jia
3. Mark Harman
4. Walid Maalej
5. Steven C. H. Hoi
6. Jialiu Lin
7. Yuanyuan Zhang
8. Xiaokui Xiao

Citations in the corpus

• A Survey of App Store Analysis for Software Engineering (2017, methodology): “[100], 2013 a Tech report 17,330 Iacob and Harrison [104], 2013 g MSR 270 Galvis Carre~ no et al.”
• How can i improve my app? Classifying user reviews for software maintenance and evolution (2015, methodology): “Iacob and Harrison [24] extracted feature requests from app reviews by means of linguistic rules and used Latent Dirichlet”
• AR-miner: mining informative reviews for developers from mobile app marketplace (2014, methodology): “[28] developed a prototype named MARA that uses a list of linguistic rules to automatically retrieve feature requests from online user reviews.”
• Bug report, feature request, or simply praise? On automatically classifying app reviews (2015, background): “A significant amount of the reviews include requirements-related information such as bugs or issues [27], summary of the user experience with certain features [12], requests for enhancements [18], and even ideas for new features [8], [27].”
• User Experiences of Social Support From Companion Chatbots in Everyday Contexts: Thematic Analysis (2020, background): “The advantages of using publicly available reviews to examine user experiences and attitudes toward a given app have been demonstrated through previous scholarly work on human-computer interactions [23-27].”
• How Do Users Like This Feature? A Fine Grained Sentiment Analysis of App Reviews (2014, methodology): “Iacob and Harrison [8] extracted feature requests from app store reviews by means of linguistic rules and used LDA to group the feature requests.”