Towards Effective Teams for the Identification of Code Smells

Abstract

Code smells are symptoms of poor design and implementation choices. Several techniques for the automated detection of code smells have been proposed, but their effectiveness is limited due to the inherent subjectivity of the task. Accepting false warnings generated by a tool may lead to unnecessary maintenance effort. Moreover, bypassing undetected smells may contribute to the software degradation. Thus, developers need to perform a subsequent manual identification of code smells to confirm their occurrences as well as address both false and missing warnings. However, performing ad hoc manual identification of smells does not assure more effective results. Indeed, different context factors may influence on the conclusion about the incidence of a code smell. Based on evidence collected from previous work, this paper presents and discusses a set of context factors that may influence the effectiveness of smell identification tasks. These factors are addressed to human aspects, such as the interaction among individuals and their professional roles. Based on such factors, we present an initial set of practical suggestions for composing more effective teams to the identification of code smells.