The application of structure and code metrics to large scale systems

This work extends the area of research termed software metrics by applying measures of system structure and measures of system code to three realistic software products. Previous research in this area has typically been limited to the application of code metrics such as: lines of code, McCabe's Cyclomatic number, and Halstead's software science variables. However, this research also investigates the relationship of four structure metrics: Henry's Information Flow measure, Woodfield's Syntactic Interconnection Model, Yau and Collofello's Stability measure and McClure's Invocation complexity, to various observed measures of complexity such as, ERRORS, CHANGES and CODING TIME. These metrics are referred to as structure measures since they measure control flow and data flow interfaces between system components.

Spearman correlations between the metrics revealed that the code metrics were similar measures of system complexity, while the structure metrics were typically measuring different dimensions of software. Furthermore, correlating the metrics to observed measures of complexity indicated that the Information Flow metric and the Invocation Measure typically performed as well as the three code metrics when project factors and subsystem factors were taken into consideration. However, it was generally true that no single metric was able to satisfactorily identify the variations in the data for a single observed measure of complexity. Trends between many of the metrics and the observed data were identified when individual components were grouped together. Code metrics typically formed groups of increasing complexity which corresponded to increases in the mean values of the observed data. The strength of the Information Flow metric and the Invocation measure is their ability to form a group containing highly complex components which was found to be populated by outliers in the observed data.