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Cited by 100Mobile and Web ApplicationsDigital Marketing and Social MediaSoftware Engineering Techniques and Practices

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Studying the dialogue between users and developers of free apps in the Google Play Store

Hassan, Safwat, Tantithamthavorn, Chakkrit, Bezemer, Cor-Paul, Hassan, Ahmed E.·2018·Empirical Software Engineering
DOI 10.1007/s10664-017-9538-9
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Overview

Integrity signals

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Crossref · 10.1007/s10664-017-9538-9

Cites

1 of 1 references verified0 citation errors · 0 not checkable

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Citations

Cites

Flagged first, then by citations

Research works printed but not linked: 25

  1. Eisenhauer JG (2009) Explanatory power and statistical significance. Teach Stat 31(2):42–46 · DOI
  2. Gehan EA (1965) A generalized Wilcoxon test for comparing arbitrarily singly-censored samples. Biometrika 52(1–2):203–223 · DOI
  3. Guzman E, Azócar D, Li Y (2014) Sentiment analysis of commit comments in GitHub: an empirical study. In: 11tH working conference on mining software repositories (MSR). IEEE, pp 352–355 · DOI
  4. Guzman E, Maalej W (2014) How do users like this feature? A fine grained sentiment analysis of app reviews. In: 22nd international requirements engineering conference (RE). IEEE, pp 153–162 · DOI
  5. Hanley JA, McNeil BJ (1982) The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143(1):29–36 · DOI · Paper page
  6. Harman M, Jia Y, Zhang Y (2012) App store mining and analysis: MSR for app stores. In: 9th working conference of mining software repositories (MSR). IEEE, pp 108–111 · DOI
  7. Iacob C, Harrison R (2013) Retrieving and analyzing mobile apps feature requests from online reviews. In: 10th working conference on mining software repositories (MSR). IEEE, pp 41–44 · DOI
  8. Iacob C, Harrison R, Faily S (2013) Online reviews as first class artifacts in mobile app development. In: 5th international conference on mobile computing, applications, and services (MobiCASE), pp 47–53
  9. Khalid H (2013) On identifying user complaints of iOS apps. In: 35th international conference on software engineering (ICSE), pp 1474–1476 · DOI
  10. Khalid H, Shihab E, Nagappan M, Hassan AE (2015) What do mobile app users complain about? IEEE Softw 32(3):70–77 · DOI
  11. Khalid M, Asif M, Shehzaib U (2015) Towards improving the quality of mobile app reviews. International Journal of Information Technology and Computer Science 35–41 · DOI
  12. Long JD, Feng D, Cliff N (2003) Ordinal analysis of behavioral data. Wiley · DOI
  13. Maalej W, Nabil H (2015) Bug report, feature request, or simply praise? on automatically classifying app reviews. In: 23rd international requirements engineering conference (RE). IEEE, pp 116–125 · DOI
  14. Martin W, Harman M, Jia Y, Sarro F, Zhang Y (2015) The app sampling problem for app store mining. In: 12th working conference on mining software repositories (MSR). IEEE/ACM, pp 123–133 · DOI
  15. Martin W, Sarro F, Jia Y, Zhang Y, Harman M (2016) A survey of app store analysis for software engineering. IEEE Trans Softw Eng (TSE) PP(99):1–32
  16. McIlroy S, Ali N, Khalid H, Hassan AE (2016) Analyzing and automatically labelling the types of user issues that are raised in mobile app reviews. Empir Softw Eng (EMSE) 21(3):1067–1106 · DOI
  17. McIlroy S, Shang W, Ali N, Hassan A (2015) Is it worth responding to reviews? a case study of the top free apps in the Google Play store. IEEE Software PP (99):1–1
  18. Moran K, Vásquez ML, Bernal-Cárdenas C, Poshyvanyk D (2015) Auto-completing bug reports for android applications. In: Proceedings of the 2015 10th joint meeting on foundations of software engineering (ESEC/FSE). ACM, pp 673–686 · DOI
  19. Oh J, Kim D, Lee U, Lee J, Song J (2013) Facilitating developer-user interactions with mobile app review digests. In: 2013 conference on human factors in computing systems (CHI). ACM SIGCHI, pp 1809–1814 · DOI
  20. Pagano D, Maalej W (2013) User feedback in the appstore: an empirical study. In: 21st international requirements engineering conference (RE). IEEE, pp 125–134 · DOI
  21. Seaman CB (1999) Qualitative methods in empirical studies of software engineering. IEEE Trans Softw Eng (TSE) 25(4):557–572 · DOI
  22. Snijders TA, Bosker RJ (2012) Multilevel analysis: an introduction to basic and advanced multilevel modeling. Sage Publications
  23. Snijders TAB (2005) Fixed and random effects. In: Encyclopedia of statistics in behavioral science. Wiley · DOI
  24. Tantithamthavorn C, McIntosh S, Hassan AE, Matsumoto K (2016) Comments on researcher bias: the use of machine learning in software defect prediction. IEEE Trans Softw Eng (TSE) 11(42):1092–1094 · DOI
  25. Tourani P, Jiang Y, Adams B (2014) Monitoring sentiment in open source mailing lists: exploratory study on the apache ecosystem. In: 24th annual international conference on computer science and software engineering (CASCON), pp 34–44

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Metadata

Authors
Hassan, S., Tantithamthavorn, C., Bezemer, C.-P., & Hassan, A. E.
Affiliations
  • Queen's University (CA)
  • The University of Adelaide (AU)
Corresponding author
Hassan, Safwat
ORCID
Venue
Empirical Software Engineering
Publisher
Springer Science and Business Media LLC
Published
2017-09-08
Volume
23
Issue
3
Pages
1275-1312
ISSN
  • 1382-3256 (ISSN-L)
  • 1573-7616
DOI
10.1007/s10664-017-9538-9
OpenAlex
W2755160617
Cited by
100
References
1
Citations by year
2026: 10 · 2025: 10 · 2024: 12 · 2023: 8 · 2022: 17 · 2021: 14 · 2020: 14 · 2019: 11 · 2018: 4
FWCI
14.04
Type
journal article
Language
en
Open access
closed
Licence
www.springer.com/tdm (tdm)
Locations
Empirical Software Engineering (publishedVersion)
Funders
Japan Society for the Promotion of Science (16J03360)
Topics
  • Physical Sciences › Computer Science › Information Systems › Mobile and Web Applications
  • Social Sciences › Business, Management and Accounting › Marketing › Digital Marketing and Social Media
  • Physical Sciences › Computer Science › Information Systems › Software Engineering Techniques and Practices
Keywords
app reviews, Google Play Store, mobile app stores, free apps, app ratings, user feedback, mixed models, app store security

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