nach oben

Empirical Software Engineering

Erschienen in:

22.02.2018

App store mining is not enough for app improvement

verfasst von: Maleknaz Nayebi, Henry Cho, Guenther Ruhe

Erschienen in: Empirical Software Engineering | Ausgabe 5/2018

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

The rise in popularity of mobile devices has led to a parallel growth in the size of the app store market, intriguing several research studies and commercial platforms on mining app stores. App store reviews are used to analyze different aspects of app development and evolution. However, app users’ feedback does not only exist on the app store. In fact, despite the large quantity of posts that are made daily on social media, the importance and value that these discussions provide remain mostly unused in the context of mobile app development. In this paper, we study how Twitter can provide complementary information to support mobile app development. By analyzing a total of 30,793 apps over a period of six weeks, we found strong correlations between the number of reviews and tweets for most apps. Moreover, through applying machine learning classifiers, topic modeling and subsequent crowd-sourcing, we successfully mined 22.4% additional feature requests and 12.89% additional bug reports from Twitter. We also found that 52.1% of all feature requests and bug reports were discussed on both tweets and reviews. In addition to finding common and unique information from Twitter and the app store, sentiment and content analysis were also performed for 70 randomly selected apps. From this, we found that tweets provided more critical and objective views on apps than reviews from the app store. These results show that app store review mining is indeed not enough; other information sources ultimately provide added value and information for app developers.

Vorheriger Artikel Program comprehension of domain-specific and general-purpose languages: replication of a family of experiments using integrated development environments

Nächster Artikel Does syntax highlighting help programming novices?

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Nur mit Berechtigung zugänglich

https://www.appannie.com.

http://www.appbrain.com.

http://www.searchman.com.

http://nutch.apache.org/.

https://support.Twitter.com/articles/64986.

Agarwal A, Xie B, Vovsha I, Rambow O, Passonneau R (2011) Sentiment analysis of twitter data. In: Proceedings of the workshop on languages in social media. Association for Computational Linguistics, pp 30–38

Benevenuto F, Magno G, Rodrigues T, Almeida V (2010) Detecting spammers on twitter. In: Collaboration, electronic messaging, anti-abuse and spam conference (CEAS), vol 6, pp 12

Blackman NJ-M, Koval J J (2000) Interval estimation for cohen’s kappa as a measure of agreement. Statist Med 19(5):723–741CrossRef

Blei D M, Ng A Y, Jordan M I (2003) Latent dirichlet allocation. J Mach Learn Res 3:993–1022MATH

Bougie G, Starke J, Storey M-A, German D M (2011) Towards understanding twitter use in software engineering: preliminary findings, ongoing challenges and future questions. In: Proceedings of the 2nd international workshop on Web 2.0 for software engineering. ACM, pp 31–36

Chang J, Gerrish S, Wang C, Boyd-Graber J L, Blei DM (2009) Reading tea leaves: How humans interpret topic models. In: Advances in neural information processing systems, pp 288–296

Chen N, Lin J, Hoi S C, Xiao X, Zhang B (2014) Ar-miner: mining informative reviews for developers from mobile app marketplace. In: Proceedings of the 36th international conference on software engineering. ACM, pp 767–778

Ciurumelea A, Schaufelbühl A, Panichella S, Gall HC (2017) Analyzing reviews and code of mobile apps for better release planning. In: Software analysis, evolution and reengineering (SANER). IEEE, pp 91– 102

Di Sorbo A, Panichella S, Alexandru C V, Shimagaki J, Visaggio C A, Canfora G, Gall H C (2016) What would users change in my app? Summarizing app reviews for recommending software changes. In: Proceedings of the 2016 24th ACM SIGSOFT international symposium on foundations of software engineering. ACM, pp 499–510

Di Sorbo A, Panichella S, Alexandru C V, Visaggio C A, Canfora G (2017) Surf: summarizer of user reviews feedback. In: Proceedings of the 39th international conference on software engineering companion. IEEE Press, pp 55–58

Gibbons J D, Chakraborti S (2011) Nonparametric statistical inference. Springer

Gomez M, Martinez M, Monperrus M, Rouvoy R (2015) When app stores listen to the crowd to fight bugs in the wild. In: Proceedings of the 37th international conference on software engineering (ICSE), vol 2. IEEE Press, pp 567–570

Gu X, Kim S (2015) What parts of your apps are loved by users? (t). In: Automated software engineering (ASE). IEEE, pp 760–770

Guzman E, Alkadhi R, Seyff N (2017) An exploratory study of twitter messages about software applications. Requir Eng 22(3):387–412CrossRef

Guzman E, Ibrahim M, Glinz M (2017) Mining twitter messages for software evolution. In: Proceedings of the 39th international conference on software engineering companion. IEEE Press, pp 283– 284

Harman M, Jia Y, Zhang Y (2012) App store mining and analysis: Msr for app stores. In: Proceedings of the 9th IEEE working conference on mining software repositories. IEEE Press, pp 108–111

Hong L, Davison B D (2010) Empirical study of topic modeling in twitter. In: Proceedings of the first workshop on social media analytics. ACM, pp 80–88

Hutto CJ, Gilbert E Vader: a parsimonious rule-based model for sentiment analysis of social media text. In: Eighth international AAAI conference on weblogs and social media, pp 50–60

Iacob C, Harrison R (2013) Retrieving and analyzing mobile apps feature requests from online reviews. In: 10th IEEE Working conference on mining software repositories (MSR). IEEE, pp 41–44

Jivani A G et al. (2011) A comparative study of stemming algorithms. Int J Comp Tech Appl 2(6):1930– 1938

Jongeling R, Datta S, Serebrenik A (2015) Choosing your weapons: on sentiment analysis tools for software engineering research. In: Software maintenance and evolution (ICSME). IEEE, pp 531– 535

Kittur A, Chi E H, Suh B (2008) Crowdsourcing user studies with mechanical turk. In: Proceedings of the SIGCHI conference on human factors in computing systems. ACM, pp 453–456

Kouloumpis E, Wilson T, Moore J D (2011) Twitter sentiment The good the bad and the omg!. Icwsm 11:538–541

Liu B (2010) Sentiment analysis and subjectivity. Handbook Natural Lang Process 2:627–666

Loper E, Bird S (2002) Nltk: the natural language toolkit. In: Proceedings of the ACL-02 workshop on effective tools and methodologies for teaching natural language processing and computational linguistics, vol 1. Association for Computational Linguistics, pp 63–70

Maalej W, Nabil H (2015) Bug report, feature request, or simply praise? On automatically classifying app reviews. In: IEEE 23rd international requirements engineering conference (RE). IEEE, pp 116– 125

Maalej W, Nayebi M, Johann T, Ruhe G (2016) Toward data-driven requirements engineering. IEEE Softw 33(1):48–54CrossRef

Manning C D, Surdeanu M, Bauer J, Finkel J R, Bethard S, McClosky D (2014) The stanford corenlp natural language processing toolkit. In: ACL (System Demonstrations), pp 55–60

Martin W, Harman M, Jia Y, Sarro F, Zhang Y (2015) The app sampling problem for app store mining. In: IEEE/ACM 12th Working conference on mining software repositories. IEEE, pp 123–133

Martin W, Sarro F, Jia Y, Zhang Y, Harman M (2016) A survey of app store analysis for software engineering. RN 16:02

Mohammad S M, Kiritchenko S, Zhu X (2013) Nrc-canada: building the state-of-the-art in sentiment analysis of tweets. arXiv:http://arXiv.org/abs/1308.6242

Naaman M, Boase J, Lai C -H (2010) Is it really about me?: message content in social awareness streams. In: Proceedings of the 2010 ACM conference on computer supported cooperative work. ACM, pp 189– 192

Nayebi M, Ruhe G (2015) Analytical product release planning. In: The Art and science of analyzing software data. Morgan Kaufmann, pp 550–580

Nayebi M, Adams B, Ruhe G (2016) Release practices for mobile apps–what do users and developers think? In: Software analysis, evolution, and reengineering (SANER), vol 1. IEEE, pp 552– 562

Nayebi M, Farrahi H, Ruhe G, Cho H (2017) App store mining is not enough. In: Proceedings of the 39th international conference on software engineering companion. IEEE Press, pp 152–154

Nayebi M, Quapp R, Ruhe G, Marbouti M, Maurer F (2017) Crowdsourced exploration of mobile app features: a case study of the fort mcmurray wildfire. In: Proceedings of the 39th international conference on software engineering: software engineering in society track. IEEE Press, pp 57–66

Nielsen F Å (2011) A new anew: Evaluation of a word list for sentiment analysis in microblogs. arXiv:http://arXiv.org/abs/1103.2903

O’Connor B, Krieger M, Ahn D (2010) Tweetmotif: exploratory search and topic summarization for twitter. In: ICWSM

Palomba F, Linares-Vásquez M, Bavota G, Oliveto R, Di Penta M, Poshyvanyk D, De Lucia A (2015) User reviews matter! tracking crowdsourced reviews to support evolution of successful apps. In: Software maintenance and evolution (ICSME). IEEE, pp 291–300

Paolacci G, Chandler J, Ipeirotis P G (2010) Running experiments on amazon mechanical turk. Judgment Decis Making 5(5):411–419

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

Prasetyo PK, Lo D, Achananuparp P, Tian Y, Lim E-P (2012) Automatic classification of software related microblogs. In: Software maintenance (ICSM). IEEE, pp 596–599

Ramage D, Dumais S T, Liebling D J (2010) Characterizing microblogs with topic models. ICWSM 10:1–1

Rosenthal S, Nakov P, Kiritchenko S, Mohammad S M, Ritter A, Stoyanov V (2015) Semeval-2015 task 10: sentiment analysis in twitter. In: Proceedings of SemEval-2015

Sebastiani F (2002) Machine learning in automated text categorization. ACM Comput Surv (CSUR) 34(1):1–47CrossRef

Smedt T D, Daelemans W (2012) Pattern for python. J Mach Learn Res 13:2063–2067MATH

Thelwall M, Buckley K, Paltoglou G (2011) Sentiment in twitter events. J Am Soc Inf Sci Technol 62(2):406–418CrossRef

Tian Y, Lo D (2014) An exploratory study on software microblogger behaviors. In: 2014 IEEE 4th Workshop on mining unstructured data (MUD). IEEE, pp 1–5

Villarroel L, Bavota G, Russo B, Oliveto R, Di Penta M (2016) Release planning of mobile apps based on user reviews. In: Proceedings of the 38th international conference on software engineering. ACM, pp 14–24

Wang X, Kuzmickaja I, Abrahamsson P (2014) Microblogging in open source software development, 8–12

Wiese IS, da Silva JT, Steinmacher I, Treude C, Gerosa MA (2016) Who is who in the mailing list? Comparing six disambiguation heuristics to identify multiple addresses of a participant. In: 2016 IEEE International conference on software maintenance and evolution (ICSME). IEEE, pp 345–355

Williams G, Mahmoud A (2017) Mining twitter data for a more responsive software engineering process. In: Proceedings of the 39th international conference on software engineering companion. IEEE Press, pp 280–282

Wohlin C, Runeson P, Höst M, Ohlsson M C, Regnell B, Wesslén A (2012) Experimentation in software engineering. Springer Science & Business Media

Titel: App store mining is not enough for app improvement
verfasst von: Maleknaz Nayebi
Henry Cho
Guenther Ruhe
Publikationsdatum: 22.02.2018
Verlag: Springer US
Erschienen in: Empirical Software Engineering / Ausgabe 5/2018
Print ISSN: 1382-3256
Elektronische ISSN: 1573-7616
DOI: https://doi.org/10.1007/s10664-018-9601-1

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Weitere Artikel der Ausgabe 5/2018

Improved representation and genetic operators for linear genetic programming for automated program repair

Towards reusing hints from past fixes

Editor’s Note: Special Issue on Automatic Software Repair

Pinpointing and repairing performance bottlenecks in concurrent programs

Studying software logging using topic models

Revisiting the performance of automated approaches for the retrieval of duplicate reports in issue tracking systems that perform just-in-time duplicate retrieval

Premium Partner