An Empirical Study on the Characteristics of Database Access Bugs in Java Applications

TL;DR

This study empirically analyzes 423 database access bugs in Java applications, revealing their root causes, common types, and differences in bug fixing, providing insights for developers and researchers to improve database access reliability.

Contribution

It categorizes the root causes of database access bugs in Java applications and quantifies their prevalence, offering a detailed empirical understanding of these bugs.

Findings

SQL query bugs account for 54% of database access bugs.

API bugs constitute 38.7% of the studied bugs.

Database and non-database bugs show similar reporting trends but differ in fix files.

Abstract

Database-backed applications rely on the database access code to interact with the underlying database management systems (DBMSs). Although many prior studies aim at database access issues like SQL anti-patterns or SQL code smells, there is a lack of study of database access bugs during the maintenance of database-backed applications. In this paper, we empirically investigate 423 database access bugs collected from seven large-scale Java open source applications that use relational database management systems (e.g., MySQL or PostgreSQL). We study the characteristics (e.g., occurrence and root causes) of the bugs by manually examining the bug reports and commit histories. We find that the number of reported database and non-database access bugs share a similar trend but their modified files in bug fixing commits are different. Additionally, we generalize categories of the root causes of database access bugs, containing five main categories (SQL queries, Schema, API, Configuration, SQL query result) and 25 unique root causes. We find that the bugs pertaining to SQL queries, Schema, and API cover 84.2% of database access bugs across all studied applications. In particular, SQL queries bug (54%) and API bug (38.7%) are the most frequent issues when using JDBC and Hibernate, respectively. Finally, we provide a discussion on the implications of our findings for developers and researchers.