A review-based comparative study of bad smell detection tools

Abstract

Bad smells are symptoms that something may be wrong in the system design or code. There are many bad smells defined in the literature and detecting them is far from trivial. Therefore, several tools have been proposed to automate bad smell detection aiming to improve software maintainability. However, we lack a detailed study for summarizing and comparing the wide range of available tools. In this paper, we first present the findings of a systematic literature review of bad smell detection tools. As results of this review, we found 84 tools; 29 of them available online for download. Altogether, these tools aim to detect 61 bad smells by relying on at least six different detection techniques. They also target different programming languages, such as Java, C, C++, and C#. Following up the systematic review, we present a comparative study of four detection tools with respect to two bad smells: Large Class and Long Method. This study relies on two software systems and three metrics for comparison: agreement, recall, and precision. Our findings support that tools provide redundant detection results for the same bad smell. Based on quantitative and qualitative data, we also discuss relevant usability issues and propose guidelines for developers of detection tools.