Empirical Assessment on Interactive Detection of Code Smells

Abstract

Code smell detection is traditionally supported by Non-Interactive Detection (NID) techniques, which enable devel-opers to reveal smells in later software versions. These techniques only reveal smells in the source code upon an explicit developer request and do not support progressive interaction with affect code. The later code smells are detected, the higher the effort to refactor the affected code. The notion of Interactive Detection (ID) has emerged to address NID's limitations. An ID technique reveals code smell instances without an explicit developer request, encouraging early detection of code smells. Even though ID seems promising, there is a lack of evidence concerning its impact on code smell detection. Our research focused on evaluating the effectiveness of the ID technique on code smell detection. For doing so, we conducted a controlled experiment where 16 subjects underwent experimental tasks. We concluded that using the ID technique led to an increase of 60% in recall and up to 13% in precision when detecting code smells. Consequently, developers could identify more refactoring opportunities using the ID technique than the NID.