Many internal software metrics and external quality attributes of Java programs correlate strongly with program size. This knowledge has been used pervasively in quantitative studies of software through practices such as normalization on size metrics. This paper reports size-related super- and sublinear effects that have not been known before. Findings obtained on a very large collection of Java programs -- 30,911 projects hosted at Google Code as of Summer 2011 -- unveils how certain characteristics of programs vary disproportionately with program size, sometimes even non-monotonically. Many of the specific parameters of nonlinear relations are reported. This result gives further insights for the differences of "programming in the small" vs. "programming in the large." The reported findings carry important consequences for OO software metrics, and software research in general: metrics that have been known to correlate with size can now be properly normalized so that all the information that is left in them is size-independent.
@cite_22 randomly collected 1,132 jar files off the Internet and analyzed them (at bytecode level) using a tool developed by the authors. The purpose of that study was to inform Java language designers and implementers about how developers actually use the language. That study reports summary statistics for their entire dataset without taking the distribution of jar size into account. Most distributions shown in the paper aren't normal, so the summary statistics are somewhat misleading. Some of the reported metrics in that study are the same metrics that we use for our study; for example they found on average 9 methods per class, with median 5. The reported values fall within the range of ours, but particularly close to the values for large projects, which leads us to believe that their dataset was biased towards large projects.
@cite_22
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We present a study of the static structure of real Java bytecode programs. A total of 1132 Java jar-files were collected from the Internet and analyzed. In addition to simple counts (number of methods per class, number of bytecode instructions per method, etc.), structural metrics such as the complexity of control-flow and inheritance graphs were computed. We believe this study will be valuable in the design of future programming languages and virtual machine instruction sets, as well as in the efficient implementation of compilers and other language processors. Copyright © 2006 John Wiley & Sons, Ltd.