Diffusion of Software Features: An Exploratory Study

New features are frequently proposed in many software libraries. These features include new methods, classes, packages, etc. These features are utilized in many open source and commercial software systems. Some of these features are adopted very quickly, while others take a long time to be adopted. Each feature takes much resource to develop, test, and document. Library developers and managers need to decide what feature to prioritize and what to develop next. As a first step to aid these stakeholders, we perform an exploratory study on the diffusion or rate of adoption of features in Java Development Kit (JDK) library. Our empirical study proposes such questions as how many new features are adopted by client applications, how long it takes for a new feature to spread to various software products, what features are diffused quickly, and what features are diffused widely. We perform an exploratory study with new features in Java Development Kit (JDK, from version 1.3 to 1.6) and provide empirical findings to answer the above research questions.

[1]  S. Sundqvist,et al.  The effects of country characteristics, cultural similarity and adoption timing on the diffusion of wireless communications , 2005 .

[2]  Chris Volinsky,et al.  Network-Based Marketing: Identifying Likely Adopters Via Consumer Networks , 2006, math/0606278.

[3]  E. Rogers,et al.  Diffusion of innovations , 1964, Encyclopedia of Sport Management.

[4]  Nachiappan Nagappan,et al.  Data analytics for game development: NIER track , 2011, 2011 33rd International Conference on Software Engineering (ICSE).

[5]  Paul Resnick,et al.  Trust among strangers in internet transactions: Empirical analysis of eBay' s reputation system , 2002, The Economics of the Internet and E-commerce.

[6]  G. Lilien,et al.  A Multi-Stage Model of Word of Mouth Through Electronic Referrals , 2004 .

[7]  Daniela E. Damian,et al.  The promises and perils of mining GitHub , 2009, MSR 2014.

[8]  Matthew Richardson,et al.  Mining knowledge-sharing sites for viral marketing , 2002, KDD.

[9]  Thomas Zimmermann,et al.  Automatic Identification of Bug-Introducing Changes , 2006, 21st IEEE/ACM International Conference on Automated Software Engineering (ASE'06).

[10]  Jon Kleinberg,et al.  Maximizing the spread of influence through a social network , 2003, KDD '03.

[11]  Daniel M. Germán,et al.  The promises and perils of mining git , 2009, 2009 6th IEEE International Working Conference on Mining Software Repositories.

[12]  D. Bowman,et al.  Managing Customer-Initiated Contacts with Manufacturers: The Impact on Share of Category Requirements and Word-of-Mouth Behavior , 2001 .

[13]  Jane Cleland-Huang,et al.  On-demand feature recommendations derived from mining public product descriptions , 2011, 2011 33rd International Conference on Software Engineering (ICSE).

[14]  Andreas Zeller,et al.  When do changes induce fixes? , 2005, ACM SIGSOFT Softw. Eng. Notes.

[15]  Ee-Peng Lim,et al.  On Modeling Virality of Twitter Content , 2011, ICADL.

[16]  Qiang Tu,et al.  Tracking structural evolution using origin analysis , 2002, IWPSE '02.

[17]  Jonathan K. Frenzen,et al.  Structure, Cooperation, and the Flow of Market Information , 1993 .

[18]  A. Montgomery Applying Quantitative Marketing Techniques to the Internet , 2000 .

[19]  Jure Leskovec,et al.  The dynamics of viral marketing , 2005, EC '06.

[20]  Michael W. Godfrey,et al.  Detecting merging and splitting using origin analysis , 2003, 10th Working Conference on Reverse Engineering, 2003. WCRE 2003. Proceedings..

[21]  Peter H. Reingen,et al.  Social Ties and Word-of-Mouth Referral Behavior , 1987 .