Empirical analysis of user data in game software development

  • Kenneth Hullett ,
  • Nachi Nagappan ,
  • Eric Schuh ,
  • John Hopson

Proceedings of the ACM-IEEE international symposium on Empirical software engineering and measurement |

Published by ACM

For several years empirical studies have spanned the spectrum of research from software productivity, quality, reliability, performance to human computer interaction. Analyses have involved software systems ranging from desktop software to telecommunication switching systems. But surprising there has been little work done on the emerging digital game industry, one of the fastest growing domains today. To the best of our knowledge, our work is one of the first empirical analysis of a large commercially successful game system. In this paper, we introduce an analysis of the significant user data generated in the gaming industry by using a successful game: Project Gotham Racing 4.

More specifically, due to the increasing ubiquity of constantly connected high-speed internet connections for game consoles, developers are able to collect extensive amounts of data about their games following release. The challenge now is to make sense of that data, and from it be able to make recommendations to developers. This paper presents an empirical case study analyzing the data collected from a released game over a three year period. The results of this analysis include a better understanding of the differences between long-term and short-term players, and the extent to which various options in the game are utilized. This led to recommendations for future development ways to reduce development costs and to keep new players engaged. A secondary goal for this paper is to introduce software game development as a topic of importance to the empirical software engineering community and discuss research results on a key difference area: data analytics on user data to customize user and development experiences.