Nowadays a vast and growing body of open source software (OSS) project data is publicly available on the internet. Despite this public body of project data, the field of software analytics has not yet settled on a solid quantitative base for basic properties such as code size, growth, team size, activity, and project failure. What is missing is a quantification of the base rates of such properties, where other fields (such as medicine) commonly rely on base rates for decision making and the evaluation of experimental results. The lack of knowledge in this area impairs both research activities in the field of software analytics and decision making on software projects in general. This paper contributes initial results of our research towards obtaining base rates using the data available at Ohloh (a large-scale index of OSS projects). Zooming in on the venerable 'lines of code' metric for code size and growth, we present and discuss summary statistics and identify further research challenges.
Software analytics is to enable software practitioners to perform data exploration and analysis in order to obtain insightful and actionable information for data-driven tasks around software and services. In this position paper, we advocate that when applying analytic technologies in practice of software analytics, one should (1) incorporate a broad spectrum of domain knowledge and expertise, e.g., management, machine learning, large-scale data processing and computing, and information visualization; and (2) investigate how practitioners take actions on the produced information, and provide effective support for such information-based action taking. Our position is based on our experiences of successful technology transfer on software analytics at Microsoft Research Asia.