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Empirical Software Engineering

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09-11-2017

Are tweets useful in the bug fixing process? An empirical study on Firefox and Chrome

Authors: Mariam El Mezouar, Feng Zhang, Ying Zou

Published in: Empirical Software Engineering | Issue 3/2018

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Abstract

When encountering an issue, technical users (e.g., developers) usually file the issue report to the issue tracking systems. But non-technical end-users are more likely to express their opinions on social network platforms, such as Twitter. For software systems (e.g., Firefox and Chrome) that have a high exposure to millions of non-technical end-users, it is important to monitor and solve issues observed by a large user base. The widely used micro-blogging site (i.e., Twitter) has millions of active users. Therefore, it can provide instant feedback on products to the developers. In this paper, we investigate whether social networks (i.e., Twitter) can improve the bug fixing process by analyzing the short messages posted by end-users on Twitter (i.e., tweets). We propose an approach to remove noisy tweets, and map the remaining tweets to bug reports. We conduct an empirical study to investigate the usefulness of Twitter in the bug fixing process. We choose two widely adopted browsers (i.e., Firefox and Chrome) that are also large and rapidly released software systems. We find that issue reports are not treated differently regardless whether users tweet about the issue or not, except that Firefox developers tend to label an issue as more severe if users tweet about it. The feedback from Firefox contributors confirms that the tweets are not currently leveraged in the bug fixing process, due to the challenges associated to discovering bugs through Twitter. Moreover, we observe that many issues are posted on Twitter earlier than on issue tracking systems. More specifically, at least one third of issues could have been reported to developers 8.2 days and 7.6 days earlier in Firefox and Chrome, respectively. In conclusion, tweets are useful in providing earlier acknowledgment of issues, which developers can potentially use to focus their efforts on the issues impacting a large user-base.

previous article Reducing user input requests to improve IT support ticket resolution process

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According to W3Schools’ browser statistics in March 2016, Chrome ranks the first with 69.9% of the usage share of browsers, followed by Firefox that has approximately 17.8% of worldwide usage share of the browsers. URL: http://www.w3schools.com/browsers/browsers_stats.asp

https://en.wikipedia.org/wiki/History_of_Firefox

https://en.wikipedia.org/wiki/Google_Chrome_release_history

https://www.chromium.org/for-testers/bug-reporting-guidelines

https://www.bugzilla.org/

https://bugs.chromium.org/p/chromium/issues/list

http://icon.shef.ac.uk/Moby/mwords.html

https://github.com/mariamelm2/TwitterPaperRep/blob/master/Data/stop_words.txt

https://twitter.com/

http://www.internetlivestats.com/twitter-statistics

http://www.socialmediatoday.com/content/top-twitter-abbreviations-you-need-know

http://www.webopedia.com/quick_ref/Twitter_Dictionary_Guide.asp

http://www.noslang.com/twitterslang.php,searchcrm.techtarget.com/definition/Twitter-chat-and-text-messaging-abbreviations

http://marketing.wtwhmedia.com/30-must-know-twitter-abbreviations-and-acronyms/

https://digiphile.wordpress.com/2009/06/11/top-50-twitter-acronyms-abbreviations-and-initialisms/

https://www.surveysystem.com/sscalc.htm

http://projects.csail.mit.edu/spatial/Stanford_Parser

https://nlp.stanford.edu/software/dependencies_manual.pdf

http://www.nltk.org/

http://lucene.apache.org/core/5_3_1/

https://lucene.apache.org/core/5_3_1/core/org/apache/lucene/search/similarities/TFIDFSimilarity.html

https://www.surveysystem.com/sscalc.htm

https://cran.r-project.org/web/packages/ldatuning

https://support.mozilla.org/en-US/

https://groups.google.com/a/chromium.org/forum/#!forum/chromium-dev

https://bugzilla.mozilla.org/show_bug.cgi?id=1361468

https://bugzilla.mozilla.org/show_bug.cgi?id=1361498

https://github.com/mariamelm2/TwitterPaperRep

https://twitter.com/

https://www.tumblr.com/

Aberdour M (2007) Achieving quality in open-source software. IEEE Soft 24 (1):58–64CrossRef

Ahmadi N, Jazayeri M, Lelli F, Nesic S (2008) A survey of social software engineering. In: ASE Workshops, IEEE, pp 1–12

Arun R, Suresh V, Veni Madhavan CE, Narasimha Murthy MN (2010) On finding the natural number of topics with latent dirichlet allocation: Some observations. In: Proceedings of the 14th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining - Volume Part I, Springer, Berlin, Heidelberg, PAKDD’10, pp 391–402CrossRef

Bacchelli A, Dal Sasso T, D’Ambros M, Lanza M (2012) Content classification of development emails. In: 2012 34th international conference on software engineering (ICSE), IEEE, pp 375–385

Baldi P F, Lopes C V, Linstead E J, Bajracharya S K (2008) A theory of aspects as latent topics. SIGPLAN Not 43(10):543–562CrossRef

Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B Methodol 57:289–300MathSciNetMATH

Benjamini Y, Yekutieli D (2001) The control of the false discovery rate in multiple testing under dependency. Ann Stat 29:1165–1188MathSciNetCrossRef

Bies A, Ferguson M, Katz K, MacIntyre R, Tredinnick V, Kim G, Marcinkiewicz M A, Schasberger B (1995) Bracketing guidelines for treebank ii style penn treebank project. University of Pennsylvania 97:100

Bird S (2006) Nltk: The natural language toolkit. In: Proceedings of the COLING/ACL on Interactive Presentation Sessions, Association for Computational Linguistics, COLING-ACL ’06, pp 69–72

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 MA, German DM (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, Web2SE ’11, pp 31–36

Buckley C, Voorhees EM (2000) Evaluating evaluation measure stability. In: Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM, New York, NY, USA, SIGIR ’00, pp 33–40

Cao J, Xia T, Li J, Zhang Y, Tang S (2009) A density-based method for adaptive lda model selection. Neurocomput 72(7-9):1775–1781CrossRef

Chen N, Lin J, Hoi SC, 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

Cliff N (1993) Dominance statistics: Ordinal analyses to answer ordinal questions. Psychol Bull 114(3):494–509CrossRef

Coelho J, Valente M T (2017) Why modern open source projects fail. In: Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering, ACM, New York, NY, USA, ESEC/FSE, vol 2017, pp 186–196

Deveaud R, SanJuan E, Bellot P (2014) Accurate and effective latent concept modeling for ad hoc information retrieval. Document numérique 17:61–84. 10.3166/DN.17.1.61-84 CrossRef

Fleiss J L (1971) Measuring nominal scale agreement among many raters. Psychol Bull 76(5):378CrossRef

Gachechiladze D, Lanubile F, Novielli N, Serebrenik A (2017) Anger and its direction in collaborative software development. In: Proceedings of the 39th International Conference on Software Engineering. IEEE Press, New Ideas and Emerging Results Track, pp 11–14

Go A, Huang L, Bhayani R (2009) Twitter sentiment analysis. Entropy 17

Griffiths T L, Steyvers M (2004) Finding scientific topics. Proc Natl Acad Sci 101(suppl 1):5228–5235CrossRef

Grissom R J, Kim JJ (2005) Effect sizes for research: A broad practical approach. Lawrence Erlbaum Associates Publishers, New Jersey

Guzzi A, Pinzger M, van Deursen A (2010) Combining micro-blogging and ide interactions to support developers in their quests. In: Proceedings of the 2010 IEEE International Conference on Software Maintenance, IEEE Computer Society, Washington, DC, USA, ICSM ’10, pp 1–5

Hill F, Reichart R, Korhonen A (2016) Simlex-999: Evaluating semantic models with (genuine) similarity estimation. Computational Linguistics

Keertipati S, Savarimuthu BTR, Licorish SA (2016) Approaches for prioritizing feature improvements extracted from app reviews. In: Proceedings of the 20th International Conference on Evaluation and Assessment in Software Engineering, ACM, p 33

Kouloumpis E, Wilson T, Moore J (2011) Twitter Sentiment Analysis: The Good the Bad and the OMG!, AAAI Press pp 538–541

Landis JR, Koch GG (1977) The measurement of observer agreement for categorical data. Biometrics 33:159–174CrossRef

Linstead E, Baldi P (2009) Mining the coherence of gnome bug reports with statistical topic models. In: 6th IEEE International Working Conference on Mining Software Repositories. MSR ’09., pp 99–102

Liu B, Zhang L (2012) A survey of opinion mining and sentiment analysis. In: Mining text data, Springer, pp 415–463

Maalej W, Nabil H (2015) Bug report, feature request, or simply praise? on automatically classifying app reviews. In: 2015 IEEE 23rd International Requirements Engineering Conference (RE), pp 116–125

MacMahon M, Stankiewicz B, Kuipers B (2006) Walk the talk: Connecting language, knowledge, and action in route instructions. Def 2(6):4

O’Connor B, Balasubramanyan R, Routledge B R, Smith N A (2010) From tweets to polls: Linking text sentiment to public opinion time series. ICWSM 11 (122-129):1–2

Pak A, Paroubek P (2010) Twitter as a corpus for sentiment analysis and opinion mining. In: LREC, vol 10, pp 1320–1326

Panichella S, Sorbo AD, Guzman E, Visaggio CA, Canfora G, Gall HC (2015) How can i improve my app? classifying user reviews for software maintenance and evolution. In: 2015 IEEE International Conference on Software Maintenance and Evolution (ICSME), pp 281–290

Picorini C (2015) Lucene-java wiki: Powered by. https://wiki.apache.org/lucene-java/PoweredBy, [Online; accessed 19-December-2016]

Piwowar H A (2011) Who shares? who doesn’t? factors associated with openly archiving raw research data. PloS one 6(7):e18,657CrossRef

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

Prasetyo PK, Lo D, Achananuparp P, Tian Y, Lim EP (2012) Automatic classification of software related microblogs. In: 28th IEEE International Conference on Software Maintenance (ICSM), pp 596–599

Reinhardt W (2009) Communication is the key - support durable knowledge sharing in software engineering by microblogging. In: Proceedings of the SENSE Workshop, Software Engineering within Social Software Environments, Germany

Romano J, Kromrey J, Coraggio J, Skowronek J (2006) Appropriate statistics for ordinal level data: Should we really be using t-test and Cohen’sd for evaluating group differences on the NSSE and other surveys?. In: Annual meeting of the Florida Association of Institutional Research, pp 1–3

Rowe S (2013) Lucene-java wiki: Lucene faq. URL https://wiki.apache.org/lucene-java/PoweredBy, [Online; accessed 19-December-2016]

Schwartz B (2014) A new click through rate study for google organic results

Sharma A, Tian Y, Lo D (2015) What’s hot in software engineering twitter space?. In: 2015 IEEE International Conference on Software Maintenance and Evolution (ICSME). IEEE, pp 541–545

Sheskin DJ (2007) Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC, Boca RatonMATH

Singer L, Figueira Filho F, Storey M A (2014) Software engineering at the speed of light: How developers stay current using twitter. In: Proceedings of the 36th International Conference on Software Engineering, ACM, New York, NY, USA, ICSE, vol 2014, pp 211–221

Socher R, Bauer J, Manning C D, Ng A Y (2013) Parsing with compositional vector grammars. In: ACL, vol 1, pp 455–465

Somasundaram K, Murphy GC (2012) Automatic categorization of bug reports using latent dirichlet allocation. In: Proceedings of the 5th India Software Engineering Conference, ISEC ’12, pp 125–130

Storey MA, Treude C, van Deursen A, Cheng LT (2010) The impact of social media on software engineering practices and tools. In: Proceedings of the FSE/SDP Workshop on Future of Software Engineering Research, ACM, New York, NY, USA, FoSER ’10, pp 359–364

Tian Y, Lo D (2014) An exploratory study on software microblogger behaviors. In: 2014 IEEE 4th Workshop on Mining Unstructured Data, pp 1–5

Tian Y, Nagappan M, Lo D, Hassan A E (2015) What are the characteristics of high-rated apps? a case study on free android applications. In: 2015 IEEE International Conference on Software Maintenance and Evolution (ICSME). IEEE, pp 301–310

Vasilescu B, Serebrenik A, Devanbu P, Filkov V (2014) How social Q&A sites are changing knowledge sharing in open source software communities. In: Proceedings of the 17th ACM Conference on Computer Supported Cooperative Work & Social Computing, ACM, CSCW ’14, pp 342–354

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

Weiss C, Premraj R, Zimmermann T, Zeller A (2007) How long will it take to fix this bug?. In: Proceedings of the Fourth International Workshop on Mining Software Repositories, IEEE Computer Society, Washington, DC, USA, MSR ’07, pp 1

Welch C (2014) Apple pulls ios 8.0.1 after users report major problems with update

Yao X, Van Durme B (2014) Information extraction over structured data: Question answering with freebase. In: ACL (1), Citeseer, pp 956–966

Yin RK (2002) Case Study Research: Design and Methods - Third Edition. 3rd edn. SAGE Publications, Thousand Oaks

Zhang F, Khomh F, Zou Y, Hassan A (2012) An empirical study on factors impacting bug fixing time. In: 2012 19th Working Conference on Reverse Engineering (WCRE), pp 225–234

Title: Are tweets useful in the bug fixing process? An empirical study on Firefox and Chrome
Authors: Mariam El Mezouar
Feng Zhang
Ying Zou
Publication date: 09-11-2017
Publisher: Springer US
Published in: Empirical Software Engineering / Issue 3/2018
Print ISSN: 1382-3256
Electronic ISSN: 1573-7616
DOI: https://doi.org/10.1007/s10664-017-9559-4

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