Database Testing Description and Features

A database is a pre-arranged collection of data that contains information and assists in data manipulation. A database can be easily managed and retrieved by the user. We can create data in tables, rows, columns, and indexes, and make it easier to define the appropriate data.

In a database, data management can become a very easy task, because we can use databases as tables to store data, functions, triggers for data manipulation, and databases to retrieve information such as displaying data representations.

In large database systems, many application and integration errors can occur that adversely affect the performance, reliability, consistency, and security of the system. we can say that it is important to test to achieve a database system that satisfies the ACID properties (Atomicity, Consistency, Isolation, and durability) of a database management system.

Introduction to Database Testing

In software testing, database testing uses the schema, tables, triggers, is the type of test used to analyze. The tested database evaluates data integrity and consistency, and this can include creating difficult queries to load and stretch the database, or reviewing its responsiveness.

In General, Data Access includes the tiered process, which includes the user interface [UI], the business layer, and the database layer.

During database testing, we can cover the following database activities:

  • Testing data integrity
  • Checking data validity
  • Performance control
  • Triggers and functions in the database
  • Testing various procedures

Why is Database Testing Important?

Database Testing is Important in software testing because it ensures data values and information received and stored into database are valid or not. Database testing helps to save data loss, saves aborted transaction data and no unauthorized access to the information. Database is important for any software application hence testers must have good knowledge of SQL for database testing.

Database Testing Categories

1.Metadata testing

Metadata testing is done to verify that the table definitions used in the case conform to the data model and application design specifications mentioned in the beginning.

2.Data Quality Testing

Data quality testing helps to verify the accuracy and quality of the data used. Data quality testing is usually done by data profiling, which helps identify any quality problems in the production system after the application has been implemented for some time.

3.Reference Data Testing

There can be situations where users come across fields that contain values that are not in the current set. These cases occur when the database fields contain a limited set of numbered values. The reference data test is used to validate such data tabs.

4.Database Procedure Unit Testing

You can always have business logistics and database procedures in an application. You must examine the structure of the database procedure and extract the test data from the program logic as part of the white box test process. At this point, the database procedural unit test comes into play.

5.Database Regression Testing

Database regression testing is done to identify any issues that might occur due to changes in the database metadata, procedures or system upgrades in your application.

6.Database Integration Testing

The whole idea integration testing is to validate that the database tables and procedures are getting populated with the expected data based on input provided in the application while being tested from the UI.

7.Database Performance Testing

Database performance is the most important element of application performance testing. Typically, the appropriate amount of data remains missing for the performance test of the database in development environments. The main reason for this is that mostly the project is very new and the database has only a small amount of test data, or the production data has PII information that cannot be loaded into the test database without scouring. Applications behave differently with different data volumes. Creating realistic test data using one of the following strategies is key to performance testing:

  • Mask and refresh test data in test environments from the production environment
  • Create a larger amount of test data using data generation tools.