Exploratory study on the effectiveness of type-level complexity metrics

Abstract

The research presented in this thesis analyzes the feasibility of using information collected at the type level of object oriented software systems as a metric for software complexity, using the number of recorded faults as the response variable. In other words, we ask the question: Do popular industrial language type systems encode enough of the model logic to provide useful information about software quality? A longitudinal case study was performed on five open source Java projects of varying sizes and domains to obtain empirical evidence supporting the proposed type level metrics. It is shown that the type level metrics Unique Morphisms and Logic per Line of Code are more strongly correlated to the number of reported faults than the popular metrics Cyclomatic Complexity and Instability, and performed comparably to Afferent Coupling, Control per Line of Code, and Depth of Inheritance Tree. However, the type level metrics did not perform as well as Efferent Coupling. In addition to looking at metrics at single points in time, successive changes in metrics between software versions was analyzed. There was insufficient evidence to suggest that the metrics reviewed in this case study provided predictive capabilities in regards to the number of faults in the system. This work is an exploratory study; reducing the threats to external validity requires further research on a wider variety of domains and languages.