In software development, data processing and storage are crucial, and databases are invaluable for any application that relies on them. Thus, database testing is a critical aspect of ensuring optimal database performance.
Database testing is a subset of software testing that involves verifying tables, triggers, other database objects, and entire database schemas. It also includes checking data integrity and consistency. Typically, this is achieved by creating and executing specific SQL queries that test the database’s structure, attributes, and functions. This article will delve into various types of database testing, methodologies, and practical applications. Let’s begin.
Table of contents
- What is database testing?
- Why database testing matters more than ever
- Types of database testing: A closer look
- Database testing steps
- Handling test data
- Conclusion
What is database testing?
Let’s start by clarifying the basics before diving into various types of database testing. What exactly is database testing, and why is it so crucial in modern software development?
Simply put, database testing involves checking and confirming the reliability and performance of a database system. Its goal is to ensure that the data stored in databases remains consistent and that the necessary processes can manipulate that data correctly to meet business requirements.
When dealing with any relational database management system (RDBMS), a comprehensive testing strategy is essential. This includes testing the code implemented in the database to ensure its correctness and to identify any potential data quality issues before they impact the application.
The components of the database that undergo testing include the database schema, all objects, and triggers. Testers use specialized SQL queries and professional database testing tools to assess database security, performance, and structure.
Currently, there are over 50 types of database testing methodologies, which can be broadly categorized into three groups: structural, functional, and non-functional testing. This article will delve into functional and non-functional testing types.
Why database testing matters more than ever
Data serves as the cornerstone of modern business, and its quality is a pivotal factor in determining efficiency. Databases integrated into various applications play a vital role in critical operations. Therefore, thorough testing of the database functionality before deployment and during operation is imperative, regardless of the technology used.
Here are some key reasons why database testing is so crucial nowadays.
Ensuring accurate data mapping
Modern applications rely on the seamless interaction between the user interface (UI) and the database (backend). This interaction may lead to potential issues, such as mismatches between source and target data types and formats, data inconsistencies (like duplicates or empty cells), incorrect data handling rules, etc. These issues can cause severe miscommunications between the UI and the backend database. Hence, comprehensive database testing has to identify and rectify these issues promptly, preventing potential problems.
Guaranteeing data accuracy and integrity
Data accuracy is about how well the data reflects real-world scenarios, while data integrity pertains to maintaining consistency from the entry point to retrieval and during storage. Given that data can undergo changes throughout its lifecycle, testing these modifications and understanding their impact on the application’s performance is vital.
Meeting business requirements
Although this aspect is less technical, it remains crucial as it concerns the data relevance. Database testers must comprehend the business requirements and ensure that the database aligns with these requirements throughout the testing process.
Types of database testing: A closer look
Let’s dive deeper into the types of database testing, which we briefly mentioned earlier: structural testing, functional testing, and non-functional testing that we briefly mentioned earlier.
Structural testing
Structural testing (or white-box testing) is the primary type of database testing that focuses on checking the database schema with all its objects, relationships between them, constraints, triggers, etc.
The types of structural testing are:
- Validating database tables and columns
- Testing the stored procedures and functions
- Index testing
- Data migration testing
- Schema testing including schema upgrade processes
- Database server testing
Functional testing
Functional testing focuses on ensuring that the product meets its functional requirements and specifications. These include UI functionality, API configuration and access, and backend database operations.
The types of functional testing are:
- Unit testing
- Integration testing
- Regression testing
- System testing
- Database testing
Non-functional testing
Non-functional testing deals with performance, load handling, server volume, stress management, etc. These aspects are critical for the overall functioning of the product but are not part of the specific functional requirements.
The types of non-functional testing include:
- Performance testing
- Scalability testing
- Load testing
- Stress testing
- Volume testing
Ideally, we should test databases and all other application features as early and as often as possible. This ongoing testing ensures data consistency, integrity, relevance, and overall product functionality throughout its lifecycle.
Unit database testing
Unit testing involves checking and validating small pieces of source code, known as units, to prove they function correctly and meet requirements. This process is carried out in isolation for each unit, ensuring it produces accurate results under specific conditions without affecting other units or the entire application.
In database testing, unit testing primarily focuses on database objects like procedures, functions, views, and rules. It’s crucial to emphasize that every code segment should undergo testing and validation before integration into the main build.
Unit testing isn’t a one-time task; it’s an ongoing process throughout the development cycle. Whenever an object is created or modified, unit tests should be developed and executed before committing changes to version control. This practice guarantees that modifications work as intended and don’t introduce adverse effects. Unit tests are also an essential component of automated Continuous Integration and Continuous Deployment (CI/CD) pipelines.
The most straightforward approach to unit testing is focusing on objects with minimal dependencies or none at all. As the number of dependencies increases, unit tests become more complex. While sophisticated unit tests are common and mastered by professionals, the primary objective of unit testing remains creating and executing straightforward tests to validate specific objects.
Integration/Regression database testing
As we’ve discussed, any application is made up of small units or modules that need thorough testing. However, even after passing unit testing, modules can sometimes encounter issues post-implementation. This is where integration testing and regression testing become crucial.
- Integration testing occurs when existing modules are linked with new ones or when new modules are added to the application. Its goal is to ensure smooth connectivity and data flow between these components.
- Regression testing comes after functional testing of new features. It aims to check overall consistency post-changes and ensure that new updates don’t impact the performance of stable features.
Both integration and regression tests should be run regularly after each build is finished. Using realistic, high-quality test data is vital to meet business requirements and produce easily interpretable results for stakeholders. For frequent builds, automating these tests is especially beneficial.
The results of integration and regression tests should be summarized accurately and promptly reported to developers. Often, these tests are carried out by specialists who are not deeply involved in the software development process, as they require a focus on business requirements.
Performance database testing
Database performance testing evaluates key aspects of a database system, including response time, resource usage, and consistency. The goal is to check how these metrics align with business needs and ensure stable performance under actual working conditions. This type of testing also examines how a high transaction load or numerous active sessions might affect data integrity.
It’s crucial to conduct performance testing alongside integration testing. This approach helps to detect and address performance issues early in the development cycle before the product is released.
The tests should use high-quality, realistic data that mirrors the characteristics and volume of live data, allowing for accurate simulation of real-world scenarios without exposing sensitive information.
Scalability database testing
Scalability testing has to ensure that an increase in workload (mostly data volumes involved) doesn’t harm performance. It’s typically done alongside performance testing as part of the overall non-functional testing process.
In practice, scalability testing uses detailed test scripts that simulate user actions and runtime data to interact effectively with the application. Professional tools make it easy to generate test data, allowing scalability tests to be seamlessly integrated into development. This proactive approach means any issues can be addressed promptly, often before finalizing the database design.
Resilience database testing
Database resilience testing assesses how well a system performs under real-world stress and challenging conditions. Essentially, it ensures that the application can handle unexpected factors like network or hardware failures and the impact of corrupted data. This testing involves simulating different usage scenarios and assessing Extract, Transform, and Load (ETL) processes.
Resilience testing is among the most advanced database tests, requiring deep expertise and manual involvement. It involves exploring various unpredictable scenarios that could impact product performance. This complexity makes automation difficult. High-quality test data, including specially modified “dirty” data that could be harmful, is crucial for these tests.
Security database testing
Database security testing evaluates the policies, configurations, and controls of a database system. The goal is to assess the implementation of security standards and best practices and identify any vulnerabilities that could lead to unauthorized access to data.
This testing involves both manual exploratory and automated penetration tests. Automated tests, which are often integrated into the development pipeline, help identify and resolve issues promptly, even in scenarios that may demand redesigning the database and application.
Security testing requires large amounts of test data to thoroughly investigate different access scenarios and confirm whether the data remains secure or has been compromised.
User-acceptance database testing
User Acceptance Testing (UAT) marks the final phase of software development or enhancement before release. Its aim is to evaluate how well the application meets requirements and performs in real-world scenarios. Typically, UAT involves customers—the end users of the database and its associated application. Additionally, developers and professional testers partake in UAT to evaluate the database’s compliance with maintainability standards.
Many experts now advocate integrating UAT into the development cycle early on, starting from the initial prototype through to the final product. This proactive approach allows teams to address issues promptly and adapt to evolving business strategies.
For UAT to be effective, it must use realistic data. Although actual production data isn’t necessary, the data should be of high quality to allow stakeholders to evaluate the application’s performance against their needs. Typically, test scenarios emerge from collaboration between testers and stakeholders, with successful scenarios often reused in subsequent tests.
Usability database testing
Usability testing complements UAT by gathering feedback from users to reveal usability aspects and issues related to interface convenience, user-friendliness, performance speed, etc. While usability testing is often part of UAT, it’s conducted by distinct teams for different purposes.
Usability tests employ various methods such as surveys, interviews, and analytics. Due to the nature of these methods, automating database usability testing proves challenging and is primarily done manually. The data used for this type of testing should closely mirror real-world scenarios to provide meaningful insights.
Database testing steps
Database testing involves constructing and executing specialized SQL queries, but it goes beyond just that. It’s a complex process that demands a deep understanding of database design, functionality, and performance, and it requires careful planning and preparation.
The steps in database testing include:
- Gathering and analyzing requirements
- Creating a detailed test plan covering strategy, scope, and timelines
- Designing test cases for all possible scenarios
- Generating test data adhering to security policies
- Writing SQL queries for test scenarios
- Setting up realistic test environments
- Running dedicated SQL queries against databases
- Collecting and evaluating results
- Documenting and reporting detected bugs
Typically, bug reporting marks the final phase of testing.
Running tests frequently is advantageous, so automating database testing whenever feasible is beneficial. This allows teams to focus on more critical tasks while repetitive and time-consuming tests run automatically. Moreover, automation is integral to modern CD/CI pipelines, ensuring more efficient software development cycles. Here, database test scripts and executions occur automatically whenever new code is implemented or as per a predefined schedule.
Handling test data
As you’ve probably noticed, most testing types require test data. The closer the test data is to live data, the better the results it can provide. However, using live data for testing poses serious security risks. To address this, organizations establish internal security protocols to determine which tests can use production data, who can conduct these tests, the access levels these testers can have, and how to manage them. Yet, this approach isn’t ideal.
An alternative is data masking, where real data is altered to meet test requirements without exposing production data. However, this requires additional security measures like encryption, which introduces technical challenges.
On the other hand, test data generation tools have advanced significantly in recent years. They can now generate fake data closely resembling live data in type, format, and quantity. Using such tools allows teams to fill database tables with data that has specific characteristics, including both “correct” and “incorrect” data cells for targeted tests.
One notable tool in this realm is dbForge Edge — an integrated development environment (IDE) for popular database management systems like MS SQL Server, MySQL/MariaDB, Oracle, and PostgreSQL. Among the many other features, Edge offers data generation tools for these databases, enabling specialists to create realistic demo data with precise settings tailored to test requirements.
Moreover, data generation tasks can be automated to consistently provide datasets meeting specific criteria for each case. dbForge Edge’s data generators can also integrate into Continuous Integration and Continuous Delivery cycles, enhancing comprehensive testing procedures for databases.
Conclusion
Software testing, particularly database testing, is a vital component of the entire development cycle. Comprehensive structural, functional, and non-functional tests should cover every minor piece of code and every performance aspect. By integrating manual and automated testing and utilizing high-quality, realistic test data, a thorough and dependable evaluation of the application’s performance can be achieved at every lifecycle stage.
Furthermore, modern database management tools have streamlined the database testing processes as an integral part of the DevOps workflow. They allow for the rapid preparation of test data with specific characteristics, easy construction of unit tests, and automation of these processes. As a result, database professionals can perform extensive testing, eliminating repetitive and monotonous tasks, and focusing on more critical development challenges.
dbForge Edge is a software solution designed for database development, management, and administration tasks across SQL Server, MySQL with MariaDB, Oracle, and PostgreSQL. It supports test data generation for these databases and automates the development and deployment processes. A fully functional 30-day free trial of dbForge Edge is available, offering an opportunity to explore its advanced features comprehensively.
