Assessing code smell interest probability: a case study

An important parameter in deciding to eliminate technical debt (TD) is the probability of a module to generate interest along software evolution. In this study, we explore code smells, which according to practitioners are the most commonly occurring type of TD in industry, by assessing the associated interest probability. As a proxy of smell interest probability we use the frequency of smell occurrences and the change proneness of the modules in which they are identified. To achieve this goal we present a case study on 47,751 methods extracted from two well-known open source projects. The results of the case study suggest that: (a) modules in which \"code smells\" are concentrated are more change-prone than smell-free modules, (b) there are specific types of \"code smells\" that are concentrated in the most change-prone modules, and (c) interest probability of code clones seems to be higher than the other two examined code smells. These results can be useful for both researchers and practitioners, in the sense that the former can focus their research on resolving \"code smells\" that produce more interest, and the latter can improve accordingly the prioritization of their repayment strategy and their training.