The authors define a software process model (SPM). The SPM is a model of evolution of the full set of documents produced in a software project. It is a general model for software development using any development approach, providing a framework for measuring, analyzing, and understanding the software development process. The authors use the model to characterize formally research in software measures and metrics. Data obtained from two software projects are presented in the SPM format.>

In attempting to describe the quality of computer software, one of the more frequently mentioned measurable attributes is complexity of the flow of control. During the past several years, there have been many attempts to quantify this aspect of computer programs, approaching the problem from such diverse points of view as graph theory and software science. Most notable measures in these areas are McCabe's cyclomatic complexity and Halstead's software effort. More recently, Woodward et al. proposed a complexity measure based on the number of crossings, or "knots," of arcs in a linearization of the flowgraph.

In attempting to describe the quality of computer software, one of the more frequently mentioned measurable attributes is complexity of the flow of control. During the past several years, there have been many attempts to quantify this aspect of computer programs, approaching the problem from such diverse points of view as graph theory and software science. Most notable measures in these areas are McCabe's cyclomatic complexity and Halstead's software effort. More recently, Woodward et al. proposed a complexity measure based on the number of crossings, or knots, of arcs in a linearization of the flowgraph.

An investigation is made into the extent to which relationships from software science are useful in analyzing programming methodology principles that are concerned with modularity. Using previously published data from over 500 programs, it is shown that the software science effort measure provides quantitative answers to questions concerning the conditions under which modularization is beneficial. Among the issues discussed are the reduction of similar code sequences by temporary variable and subprogram defmition, and the use of global variables. Using data flow analysis, environmental considerations which affect the applicability of alternative modularity techniques are also discussed.