Understanding metric-based detectable smells in Python software: A comparative study

Abstract Context Code smells are supposed to cause potential comprehension and maintenance problems in software development. Although code smells are studied in many languages, e.g. Java and C#, there is a lack of technique or tool support addressing code smells in Python. Objective Due to the great differences between Python and static languages, the goal of this study is to define and detect code smells in Python programs and to explore the effects of Python smells on software maintainability. Method In this paper, we introduced ten code smells and established a metric-based detection method with three different filtering strategies to specify metric thresholds (Experience-Based Strategy, Statistics-Based Strategy, and Tuning Machine Strategy). Then, we performed a comparative study to investigate how three detection strategies perform in detecting Python smells and how these smells affect software maintainability with different detection strategies. This study utilized a corpus of 106 Python projects with most stars on GitHub. Results The results showed that: (1) the metric-based detection approach performs well in detecting Python smells and Tuning Machine Strategy achieves the best accuracy; (2) the three detection strategies discover some different smell occurrences, and Long Parameter List and Long Method are more prevalent than other smells; (3) several kinds of code smells are more significantly related to changes or faults in Python modules. Conclusion These findings reveal the key features of Python smells and also provide a guideline for the choice of detection strategy in detecting and analyzing Python smells.