Characterizing the Relative Significance of a Test Smell

Abstract

Test code, just like any other code we write, erodes when frequently changed. As such, refactoring, which has been shown to impact maintainability and comprehensibility, can be part of a solution to counter this erosion. We propose a metric-based heuristical approach, which allows to rank occurrences of so-called test smells (i.e. symptoms of poorly designed tests) according to their relative significance. This ranking can subsequently be used to start refactoring. Through an open-source case study, ArgoUML, we demonstrate that we are able to identify those test cases who violate unit test criteria