Exploiting dynamic analysis for architectural smell detection: a preliminary study

Abstract

Architectural anomalies, also known as architectural smells, represent the violation of design principles or decisions that impact internal software qualities with significant negative effects on maintenance, evolution costs and technical debt. Architectural smells, if early removed, have an overall impact on reducing a possible progressive architectural erosion and architectural debt. Some tools have been proposed for their detection, exploiting different methods, usually based only on static analysis. This work analyzes how dynamic analysis can be exploited to detect architectural smells. We focus on two smells, Hub-Like Dependency and Cyclic Dependency, and we extend an existing tool integrating dynamic analysis. We conduct an empirical study on ten projects. We compare the results obtained comparing a method featuring dynamic analysis and the original version of Arcan based only on static analysis to understand if dynamic analysis can be successfully used. The results show that dynamic analysis helps identify missing architectural smells instances, although its usage is hindered by the lack of test suites suitable for this scope.