Studying Duplicate Logging Statements and Their Relationships with Code Clones

TL;DR

This study investigates duplicate logging statements in large open source systems, identifies problematic patterns, develops an automated detection tool, and explores their relationship with code clones, revealing significant prevalence and maintenance challenges.

Contribution

The paper introduces DLFinder, an automated tool for detecting problematic duplicate logging code smells, validated through manual analysis and developer feedback, and explores their connection with code clones.

Findings

83% of duplicate logging smells are in cloned code

17% of smells are in micro-clones hard to detect

Over half of duplicate logs are in short code snippets

Abstract

In this paper, we focus on studying duplicate logging statements, which are logging statements that have the same static text message. We manually studied over 4K duplicate logging statements and their surrounding code in five large-scale open source systems. We uncovered five patterns of duplicate logging code smells. For each instance of the duplicate logging code smell, we further manually identify the potentially problematic and justifiable cases. Then, we contact developers to verify our manual study result. We integrated our manual study result and the feedback of developers into our automated static analysis tool, DLFinder, which automatically detects problematic duplicate logging code smells. We evaluated DLFinder on the five manually studied systems and three additional systems. In total, combining the results of DLFinder and our manual analysis, we reported 91 problematic duplicate logging code smell instances to developers and all of them have been fixed. We further study the relationship between duplicate logging statements, including the problematic instances of duplicate logging code smells, and code clones. We find that 83% of the duplicate logging code smell instances reside in cloned code, but 17% of them reside in micro-clones that are difficult to detect using automated clone detection tools. We also find that more than half of the duplicate logging statements reside in cloned code snippets, and a large portion of them reside in very short code blocks which may not be effectively detected by existing code clone detection tools. Our study shows that, in addition to general source code that implements the business logic, code clones may also result in bad logging practices that could increase maintenance difficulties.