MODELING THE RELATIONSHIP BETWEEN SOURCE CODE COMPLEXITY AND MAINTENANCE DIFFICULTY

While complexity and difficulty are abstract concepts and therefore no t directly observable, they are nevertheless indicated by observable p henomena. The study described in this article was undertaken to model the relationship between source code complexity and maintenance diffic ulty. This is achieved by applying canonical correlation analysis. Pro duct and process measures collected during the development of a commer cial real-time product provided the data for the analysis. Sets of the se measures represent source code complexity and maintenance difficult y. The authors hypothesize that source code complexity exerts a causal influence on maintenance difficulty experienced during the system tes t phase of the product. They demonstrate that significant canonical co rrelations along two dimensions support this hypothesis. Interpretatio n of these two dimensions of canonical correlation reveals relationshi ps between the sets of manifest variables that were not immediately ap parent from their simple correlations. Specifically, the model suggest s that two subsets of product measures have different relationships wi th process activity. One is related to design-change activity that res ulted in faults, and the other is related directly to faults. The auth ors conclude that soft models of greater generality than canonical cor relation could provide more insight into relationships among software engineering measures. However, much work remains to specify subsets of indicators and development efforts for which the technique could be u seful as a predictive tool.