Identification of Code Properties that Support Code Smell Analysis

Code smells are structures in code that imply potential maintainability problems and may negatively impact software quality. One of the critical challenges with code smells is that their definitions are often vague, difficult to comprehend and subjective, making them hard to reliably and consistently detect and analyze by humans and automated systems. Most existing code smell detection approaches rely heavily on human interpretation and are typically supported by structural code metrics. Unfortunately, many of these approaches are incomplete and do not cover a range of code properties that could indicate potential code smells.This paper analyzes code smell detection approaches to identify code properties used for code smell detection and analysis. Informed by our previous work and the literature, we define five code properties used by humans and automatic detectors to identify code smells. We demonstrate how various code properties can be mapped to the 22 code smells defined by Martin Fowler. The resulting catalog of properties can help software engineers and code maintainability researchers analyze code smells and build automated code smell detectors that examine properties beyond the traditional structural metrics.