Linking User Requests, Developer Responses and Code Changes: Android OS Case Study

Since software systems are designed to satisfy customers' needs, developers have an obligation to address users' requirements and demands logged via issue trackers and other forums. Having to respond to a large number of requests while developing and perfecting systems presents prioritization challenges, however. Android Operating System (OS) developers have largely overcome this obstacle by responding to specific user requests, which may be traced back to actual software code changes, providing lessons for the software engineering community. This study applies text and data mining techniques to investigate the Android community as an ecosystem, exploring how developers responded to issues raised by the community over several versions of the OS. Results show a strong relationship between issues raised by the community and developer responses to these issues. This relationship also extended to actual source code changes made by developers. Furthermore, the findings show a correlation between user requests and developer responses enacted via code changes across specific Android versions and important functionalities. This evidence suggests that developers have invested in the Android platform to guarantee its survival and overall success, largely through addressing user demands. We outline implications for software engineering professionals and software systems success.

[1]  Ning Chen,et al.  AR-miner: mining informative reviews for developers from mobile app marketplace , 2014, ICSE.

[2]  Dewayne E. Perry,et al.  Implications of evolution metrics on software maintenance , 1998, Proceedings. International Conference on Software Maintenance (Cat. No. 98CB36272).

[3]  Per Runeson,et al.  Guidelines for conducting and reporting case study research in software engineering , 2009, Empirical Software Engineering.

[4]  Bernd Brügge,et al.  User Feedback in Mobile Development , 2014, MobileDeLi '14.

[5]  Sherlock A. Licorish,et al.  The true role of active communicators: an empirical study of Jazz core developers , 2013, EASE '13.

[6]  Liudmila Ulanova,et al.  An Empirical Analysis of Bug Reports and Bug Fixing in Open Source Android Apps , 2013, 2013 17th European Conference on Software Maintenance and Reengineering.

[7]  Sherlock A. Licorish,et al.  Analyzing confidentiality and privacy concerns: insights from Android issue logs , 2015, EASE.

[8]  Anthony Finkelstein,et al.  Ieee Transactions on Software Engineering, Manuscript Id Stakerare: Using Social Networks and Collaborative Filtering for Large-scale Requirements Elicitation , 2022 .

[9]  Sherlock A. Licorish Exploring the Prevalence and Evolution of Android Concerns: A Community Viewpoint , 2016, J. Softw..

[10]  Bastin Tony Roy Savarimuthu,et al.  They'll Know It When They See It: Analyzing Post-Release Feedback from the Android Community , 2015, AMCIS.

[11]  Sherlock A. Licorish,et al.  Exploring the links between software development task type, team attitudes and task completion performance: Insights from the Jazz repository , 2017, Inf. Softw. Technol..

[12]  David A. Wagner,et al.  Do Android users write about electric sheep? Examining consumer reviews in Google Play , 2013, 2013 IEEE 10th Consumer Communications and Networking Conference (CCNC).

[13]  O. Holsti Content Analysis for the Social Sciences and Humanities , 1969 .

[14]  Bastin Tony Roy Savarimuthu,et al.  Attributes that Predict which Features to Fix: Lessons for App Store Mining , 2017, EASE.

[15]  Margaret Butler,et al.  Android: Changing the Mobile Landscape , 2011, IEEE Pervasive Computing.

[16]  Björn Regnell,et al.  Market-Driven Requirements Engineering for Software Products , 2005 .

[17]  Amjed Tahir,et al.  On Satisfying the Android OS Community: User Feedback Still Central to Developers' Portfolios , 2015, 2015 24th Australasian Software Engineering Conference.

[18]  Gabriele Bavota,et al.  User reviews matter! Tracking crowdsourced reviews to support evolution of successful apps , 2015, 2015 IEEE International Conference on Software Maintenance and Evolution (ICSME).

[19]  Dan Klein,et al.  Feature-Rich Part-of-Speech Tagging with a Cyclic Dependency Network , 2003, NAACL.

[20]  Harald C. Gall,et al.  How can i improve my app? Classifying user reviews for software maintenance and evolution , 2015, 2015 IEEE International Conference on Software Maintenance and Evolution (ICSME).

[21]  Mahmood Hosseini,et al.  Towards Crowdsourcing for Requirements Engineering , 2014, REFSQ Workshops.

[22]  Gabriele Bavota,et al.  Release Planning of Mobile Apps Based on User Reviews , 2016, 2016 IEEE/ACM 38th International Conference on Software Engineering (ICSE).

[23]  Shamsul Sahibuddin,et al.  Critical success factors for software projects: A comparative study , 2011 .

[24]  Hinrich Schütze,et al.  Book Reviews: Foundations of Statistical Natural Language Processing , 1999, CL.

[25]  Daniel M. Berry Aybüke Aurum and Claes Wohlin (eds): Engineering and managing software requirements , 2006, Requirements Engineering.

[26]  Björn Regnell,et al.  Speeding up requirements management in a product software company: linking customer wishes to product requirements through linguistic engineering , 2004, Proceedings. 12th IEEE International Requirements Engineering Conference, 2004..