On the Prevalence, Impact, and Evolution of SQL Code Smells in Data-Intensive Systems

TL;DR

This study empirically investigates the prevalence, evolution, and impact of SQL code smells in data-intensive open-source systems, revealing their persistence and weak correlation with traditional code smells and bugs.

Contribution

It provides the first comprehensive empirical analysis of SQL code smells, highlighting their prevalence, evolution, and the need for dedicated detection and refactoring tools.

Findings

SQL code smells are common in data-intensive systems.

SQL code smells have weak co-occurrence with traditional smells.

SQL code smells are less associated with bugs than traditional smells.

Abstract

Code smells indicate software design problems that harm software quality. Data-intensive systems that frequently access databases often suffer from SQL code smells besides the traditional smells. While there have been extensive studies on traditional code smells, recently, there has been a growing interest in SQL code smells. In this paper, we conduct an empirical study to investigate the prevalence and evolution of SQL code smells in open-source, data-intensive systems. We collected 150 projects and examined both traditional and SQL code smells in these projects. Our investigation delivers several important findings. First, SQL code smells are indeed prevalent in data-intensive software systems. Second, SQL code smells have a weak co-occurrence with traditional code smells. Third, SQL code smells have a weaker association with bugs than that of traditional code smells. Fourth, SQL code smells are more likely to be introduced at the beginning of the project lifetime and likely to be left in the code without a fix, compared to traditional code smells. Overall, our results show that SQL code smells are indeed prevalent and persistent in the studied data-intensive software systems. Developers should be aware of these smells and consider detecting and refactoring SQL code smells and traditional code smells separately, using dedicated tools.