research-article

SURF: summarizer of user reviews feedback

Authors:

Andrea Di Sorbo,

Sebastiano Panichella,

Carol V. Alexandru,

Corrado A. Visaggio,

Gerardo CanforaAuthors Info & Claims

ICSE-C '17: Proceedings of the 39th International Conference on Software Engineering Companion

Pages 55 - 58

https://doi.org/10.1109/ICSE-C.2017.5

Published: 20 May 2017 Publication History

Abstract

Continuous Delivery (CD) enables mobile developers to release small, high quality chunks of working software in a rapid manner. However, faster delivery and a higher software quality do neither guarantee user satisfaction nor positive business outcomes. Previous work demonstrates that app reviews may contain crucial information that can guide developer's software maintenance efforts to obtain higher customer satisfaction. However, previous work also proves the difficulties encountered by developers in manually analyzing this rich source of data, namely (i) the huge amount of reviews an app may receive on a daily basis and (ii) the unstructured nature of their content. In this paper, we propose SURF (Summarizer of User Reviews Feedback), a tool able to (i) analyze and classify the information contained in app reviews and (ii) distill actionable change tasks for improving mobile applications. Specifically, SURF performs a systematic summarization of thousands of user reviews through the generation of an interactive, structured and condensed agenda of recommended software changes. An end-to-end evaluation of SURF, involving 2622 reviews related to 12 different mobile applications, demonstrates the high accuracy of SURF in summarizing user reviews content. In evaluating our approach we also involve the original developers of some apps, who confirm the practical usefulness of the software change recommendations made by SURF.

Demo URL: https://youtu.be/Yf-U5ylJXvo

Demo webpage: http://www.ifi.uzh.ch/en/seal/people/panichella/tools/SURFTool.html

References

[1]

N. Chen, J. Lin, S. C. H. Hoi, X. Xiao, and B. Zhang. Ar-miner: Mining informative reviews for developers from mobile app marketplace. In Proceedings of the 36th International Conference on Software Engineering, ICSE 2014, pages 767--778, New York, NY, USA, 2014. ACM.

Digital Library

[2]

M.-C. de Marneffe and C. D. Manning. The stanford typed dependencies representation. In Coling 2008: Proceedings of the Workshop on Cross-Framework and Cross-Domain Parser Evaluation, CrossParser '08, pages 1--8, Stroudsburg, PA, USA, 2008. Association for Computational Linguistics.

Digital Library

[3]

A. Di Sorbo, S. Panichella, C. Alexandru, J. Shimagaki, C. Visaggio, G. Canfora, and H. Gall. What would users change in my app? summarizing app reviews for recommending software changes. In Foundations of Software Engineering (FSE), 2016 ACM SIGSOFT International Symposium on the, pages 499--510, 2016.

Digital Library

[4]

A. Di Sorbo, S. Panichella, C. A. Visaggio, M. Di Penta, G. Canfora, and H. C. Gall. Development emails content analyzer: Intention mining in developer discussions (T). In 30th IEEE/ACM International Conference on Automated Software Engineering, ASE 2015, Lincoln, NE, USA, November 9--13, 2015, pages 12--23, 2015.

Digital Library

[5]

A. Di Sorbo, S. Panichella, C. A. Visaggio, M. Di Penta, G. Canfora, and H. C. Gall. DECA: development emails content analyzer. In Proceedings of the 38th International Conference on Software Engineering, ICSE 2016, Austin, TX, USA, May 14--22, 2016 - Companion Volume, pages 641--644, 2016.

Digital Library

[6]

X. Gu and S. Kim. What parts of your apps are loved by users? (T). In 30th IEEE/ACM International Conference on Automated Software Engineering, ASE 2015, Lincoln, NE, USA, November 9--13, 2015, pages 760--770, 2015.

Digital Library

[7]

M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten. The weka data mining software: An update. SIGKDD Explor. Newsl., 11(1):10--18, Nov. 2009.

Digital Library

[8]

S. Krusche and B. Bruegge. User feedback in mobile development. In Proceedings of the 2Nd International Workshop on Mobile Development Lifecycle, MobileDeLi '14, pages 25--26, New York, NY, USA, 2014. ACM.

Digital Library

[9]

W. Maalej and H. Nabil. Bug report, feature request, or simply praise? on automatically classifying app reviews. In Requirements Engineering Conference (RE), 2015 IEEE 23rd International, Aug 2015.

[10]

C. D. Manning, M. Surdeanu, J. Bauer, J. Finkel, S. J. Bethard, and D. McClosky. The Stanford CoreNLP natural language processing toolkit. In Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pages 55--60, 2014.

[11]

D. Pagano and B. Brügge. User involvement in software evolution practice: A case study. In Proceedings of the 2013 International Conference on Software Engineering, ICSE '13, pages 953--962, Piscataway, NJ, USA, 2013. IEEE Press.

Digital Library

[12]

D. Pagano and W. Maalej. User feedback in the appstore: An empirical study. In Proceedings of the 21st IEEE International Requirements Engineering Conference (RE 2013). IEEE Computer Society, 2013.

[13]

F. Palomba, M. Linares-Vasquez, G. Bavota, R. Oliveto, M. Di Penta, D. Poshyvanyk, and A. De Lucia. User reviews matter! tracking crowdsourced reviews to support evolution of successful apps. In Software Maintenance and Evolution (ICSME), 2015 IEEE International Conference on, pages 291--300, Sept 2015.

Digital Library

[14]

S. Panichella, A. Di Sorbo, E. Guzman, C. Visaggio, G. Canfora, and H. Gall. How can I improve my app? classifying user reviews for software maintenance and evolution. In Software Maintenance and Evolution (ICSME), 2015 IEEE International Conference on, pages 281--290, Sept 2015.

Digital Library

[15]

S. Panichella, A. Di Sorbo, E. Guzman, C. Visaggio, G. Canfora, and H. Gall. Ardoc: App reviews development oriented classifier. In Foundations of Software Engineering (FSE), 2016 ACM SIGSOFT International Symposium on the, pages 1023--1027, 2016.

Digital Library

[16]

M. F. Porter. An algorithm for suffix stripping. Program, 14(3):130--137, 1980.

Cited By

Wei JCourbis ALambolais TXu BBernard PDray GMaalej WFilkov VRay BZhou M(2024)Getting Inspiration for Feature Elicitation: App Store- vs. LLM-based ApproachProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695591(857-869)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3691620.3695591
Di Sorbo AZampetti FVisaggio ADi Penta MPanichella S(2023)Automated Identification and Qualitative Characterization of Safety Concerns Reported in UAV Software PlatformsACM Transactions on Software Engineering and Methodology10.1145/356482132:3(1-37)Online publication date: 26-Apr-2023
https://dl.acm.org/doi/10.1145/3564821
Oliveira ADos Santos PMarcílio Júnior WAljedaani WEler DEler M(2023)Analyzing Accessibility Reviews Associated with Visual Disabilities or Eye ConditionsProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581315(1-14)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3581315
Show More Cited By

Recommendations

ARdoc: app reviews development oriented classifier
FSE 2016: Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering

Google Play, Apple App Store and Windows Phone Store are well known distribution platforms where users can download mobile apps, rate them and write review comments about the apps they are using. Previous research studies demonstrated that these ...
Software Analytics for Mobile Applications--Insights & Lessons Learned
CSMR '13: Proceedings of the 2013 17th European Conference on Software Maintenance and Reengineering

Mobile applications, known as apps, are software systems running on handheld devices, such as smartphones and tablet PCs. The market of apps has rapidly expanded in the past few years into a multi-billion dollar business. Being a new phenomenon, it is ...
A study of the relation of mobile device attributes with the user-perceived quality of Android apps

The number of mobile applications (apps) and mobile devices has increased considerably over the past few years. Online app markets, such as the Google Play Store, use a star-rating mechanism to quantify the user-perceived quality of mobile apps. Users ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

ICSE-C '17: Proceedings of the 39th International Conference on Software Engineering Companion

May 2017

558 pages

ISBN:9781538615898

General Chair:
Sebastian Uchitel
University of Buenos Aires, Argentina and Imperial College London, UK
,
Program Chairs:
Alessandro Orso
Georgia Institute of Technology
,
Martin Robillard
McGill University, Canada

Sponsors

SIGSOFT: ACM Special Interest Group on Software Engineering
IEEE-CS: Computer Society
SADIO: SADIO

Publisher

IEEE Press

Publication History

Published: 20 May 2017

Check for updates

Author Tags

Qualifiers

Research-article

Conference

ICSE '17

Sponsor:

SIGSOFT
IEEE-CS
SADIO

ICSE '17: 39th International Conference on Software Engineering

May 20 - 28, 2017

Buenos Aires, Argentina

Acceptance Rates

Overall Acceptance Rate 276 of 1,856 submissions, 15%

Upcoming Conference

ICSE 2025

2025 IEEE/ACM 46th International Conference on Software Engineering

April 26 - May 3, 2025

Ottawa , ON , Canada

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

15
Total Citations
View Citations
103
Total Downloads

Downloads (Last 12 months)4
Downloads (Last 6 weeks)2

Reflects downloads up to 01 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Wei JCourbis ALambolais TXu BBernard PDray GMaalej WFilkov VRay BZhou M(2024)Getting Inspiration for Feature Elicitation: App Store- vs. LLM-based ApproachProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695591(857-869)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3691620.3695591
Di Sorbo AZampetti FVisaggio ADi Penta MPanichella S(2023)Automated Identification and Qualitative Characterization of Safety Concerns Reported in UAV Software PlatformsACM Transactions on Software Engineering and Methodology10.1145/356482132:3(1-37)Online publication date: 26-Apr-2023
https://dl.acm.org/doi/10.1145/3564821
Oliveira ADos Santos PMarcílio Júnior WAljedaani WEler DEler M(2023)Analyzing Accessibility Reviews Associated with Visual Disabilities or Eye ConditionsProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581315(1-14)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3581315
Alshangiti MShi WLima ELiu XYu QRoychoudhury ACadar CKim M(2022)Hierarchical Bayesian multi-kernel learning for integrated classification and summarization of app reviewsProceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3540250.3549174(558-569)Online publication date: 7-Nov-2022
https://dl.acm.org/doi/10.1145/3540250.3549174
Hu XGuo YLu JZhu ZLi CGe JHuang LLuo BRoychoudhury ACadar CKim M(2022)Lighting up supervised learning in user review-based code localization: dataset and benchmarkProceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3540250.3549141(533-545)Online publication date: 7-Nov-2022
https://dl.acm.org/doi/10.1145/3540250.3549141
Izadi MAhmadabadi MSorbo APanichella S(2022)On the evaluation of NLP-based models for software engineeringProceedings of the 1st International Workshop on Natural Language-based Software Engineering10.1145/3528588.3528665(48-50)Online publication date: 21-May-2022
https://dl.acm.org/doi/10.1145/3528588.3528665
Gôlo MAraújo ARossi RMarcacini R(2022)Detecting relevant app reviews for software evolution and maintenance through multimodal one-class learningInformation and Software Technology10.1016/j.infsof.2022.106998151:COnline publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1016/j.infsof.2022.106998
AlOmar EAljedaani WTamjeed MMkaouer MEl-Glaly YKitamura YQuigley AIsbister KIgarashi TBjørn PDrucker S(2021)Finding the Needle in a Haystack: On the Automatic Identification of Accessibility User ReviewsProceedings of the 2021 CHI Conference on Human Factors in Computing Systems10.1145/3411764.3445281(1-15)Online publication date: 6-May-2021
https://dl.acm.org/doi/10.1145/3411764.3445281
Hu YWang HJi TXiao XLuo XGao PGuo Y(2021)CHAMPProceedings of the 43rd International Conference on Software Engineering10.1109/ICSE43902.2021.00089(933-945)Online publication date: 22-May-2021
https://dl.acm.org/doi/10.1109/ICSE43902.2021.00089
Eler MOrlandin LOliveira AGuimaraes RSaibel Santos Cde Santana VKronbauer A(2019)Do Android app users care about accessibility?Proceedings of the 18th Brazilian Symposium on Human Factors in Computing Systems10.1145/3357155.3358477(1-11)Online publication date: 22-Oct-2019
https://dl.acm.org/doi/10.1145/3357155.3358477
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents