What is Database Testing?
The database is an essential part of any system or application. Everything exists by data. If there is no data, nothing is possible. Its instability may cause the systems to behave unexpectedly. Either it gets crashed, or the data is stored in an unorganized way, in both the cases data will become useless. So it helps us to find out such vulnerabilities in a database system to protect it from an unstable state.
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What are the challenges?
The below highlighted are the challenges of Database Testing:
Under Testing
DB testing requires an organized, well-planned approach. When the testing team doesn't have enough understanding, testing becomes lengthy and incomplete in terms of test coverage and the test data utilized.The Incomplete Scope of Testing
Under-qualified testing teams perform just back-box testing. They test the applications that depend on it by acting like real users; they test only through the user interface (UI). White-box testing provides a gives idea as to test coverage, but requires testers with excellent SQL skills. Those testers cost more, yet the cost is maintained, as SQL queries also work for testing triggers, procedures, and other critical database properties.
Inexperienced testing teams neglect to prioritize DB features in terms of significance, which is essential to saving effort and time. They try to test everything, then lose time by testing minor pieces of it, and leaving the significant parts under-tested or not tested at all.
Incomplete Test Data
Most common testing approach is to populate a database with a limited amount of false data. These data have nothing to work with the real data, so the testing team can't recognize some flaws. When finally uncover these issues, need to invest additional time and effort debugging and retesting. It's possible to utilize realistic test data without exposing actual customer data. While can't use the names of real people and companies, you can use postal codes, Phone numbers specific to a target country. And that may help to address any front-end inconsistencies that users might experience adequately.Pricey automation
The problem is that testing teams are unaware of the cost of tools. They tend to go for open source tools, hoping to get a performing tool for nothing. These tools may provide rich functionality free of charge, but open source tools may also lack the stability and convenience of commercial tools. For better service, you'll need to pay, so if decide to go with a commercial tool, it's critical to calculate costs correctly. To do that need a good understanding of the project specifics and tool functionality required.What are the benefits?
- It speeds an overall cycle to create a room to test large data sets.
- It also helps in processing complex transaction behaviors.
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What are the best practices?
- Read, Write, Trigger Tables - It involves reading, writing, insertion, updation, and deletion of data to be processed. Triggering of tables involves Validation of modified records.
- BackUp Tables - It includes Continuous Execution and backup of modified data followed by testing including backup of write tables.
- Protect Target Environment and determine which code safe to deploy.
- Availability of Database Administrators.
- Test Environment Configuration.
- Analyze the predicted needs of hardware, software, resources.
- Implement Scalability and Security of the schema.
- Deliver small and frequent releases to determine issues and get instant feedback.
- Tasks Automation.
- Timely execution of scheduled jobs.
What are the types of testing?
Sometimes changes in DB and testing available limited copies of databases may also cause the failure of the system hence tester should take care that while testing extra copy of data should be available. While DB testing, it not only covers unit testing for it. Instead of that it also includes many types of testing executed at a database.
Non-functional Testing
Non-functional training comprises load testing, stress testing, ensuring that minimum system criteria are met in order to satisfy business specifications, risk identification, and its performance optimization. Tests can include are
- Security testing - It is a type of Testing that identifies the system vulnerabilities, threats, risks and prevents malicious attacks from intruders.
- Stress testing - With Stress Testing evaluates the robustness and error-handling capabilities under harsh load conditions.
- Volume testing - System performance is analyzed by increasing the volume of data in the database.
- Load Testing - This test determines how it behaves while being accessed by multiple users simultaneously
Functional Database Testing
Functional testing is carried out from the perspective of the end-user, to determine if the necessary transactions and activities carried out by the end-users fulfill the business requirements.
White box
- Testing database triggers and logical views
- Validating data models and schema.
- Checking referential integrity and consistency.
- Use white box testing techniques such as statement coverage, decision coverage, condition coverage.
Black Box
- Data Mapping.
- Verifying stored and retrieved data.
- Uses black box testing techniques such as equivalence partitioning and boundary value analysis.
Structural Database Testing
Structural database checking entails the verification of all items within the data repository that are exclusively used for data collection and cannot be directly accessed by end users. In these types of tests, its server validation is also a critical factor to remember. The testers must master SQL in order to complete this process successfully.
- Schema / Mapping Testing - It entails using database object mapping to validate the objects of a front-end application.
- Stored Procedures Testing - This test ensures that manual execution of stored procedure and view generate desired results.
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Testing on Multiple Databases
There are different kind of databases available at which testing is possible. These are explained below -
- Production - This database comes with live data hence testing cannot be performed on this data.
- Local development - In this most of the testing is carried out.
- Populated development - This database is shared by all developers, to run an application. This is used to check that our application can work with a large amount of data, instead of testing only a few records which are present in a test. Ensures application can work with a large amount of data.
- Deployment - In this the tests are run before deployment to make sure all the local database changes applied to it.
Database Migrations
There is a need for its migration while doing database testing. DB migration allows for following things -
- Recreate the database from scratch.
- Clarify the state of the database.
- Migrate from the current version of the database to a newer one.
How does it work?
- Write test cases using Db Unit for a specific database supported by DbUnit.
- Specify a DbUnit test case for the project in a file named as filename_test.go.
- While the execution of test cases actual and expected values are compared.
- Result fails if the expected value from it after the execution of the result is not equal to the expected value.
- Result succeeds if the expected value from it after the execution of the result is equal to the expected value.
DB Unit
- Db Unit is a J Unit extension target at database-driven projects put the database into a known state between test runs.
- It mainly focuses on testing of relational databases.
- Avoid myriad of problems that occur when one test case corrupts the database and causes subsequent tests to fail or exacerbate the damage.
- Db-unit can export and import your database data to and from XML datasets.
- Since version 2.0, Db-unit can also work with massive datasets when used in streaming mode. Db-unit can also help you to verify that your database data match an expected set of values.
Here Db unit is used to testing on PostgreSQL DB -
- Db-unit is ideal as an add-on testing framework which relies heavily on databases.
- Db-unit comes in need to do an integration testing.
Some of the databases supported by a DB Unit -
- Oracle
- SQL Server
- MySQL
- DB2
- PostgreSQL
- HSQLDB
- Derby
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How to adopt Database Testing?
In this demo, the actual result checked with the expected result. The demo built by the following things -
- The database used: PostgreSQL
- The language used: GoLang
- Automation for: DB unit
Write test cases for the database using DB unit.
- In this data present in it is checked, On a basis, if a data comes after the execution of the query from the database is equal to an expected result.
- The result comes after the execution of the query compared with the actual result.
The following steps need to be followed to run test cases using DB unit. These steps written below can be easily understood by looking at a flow mentioned above diagram.
- Firstly write test cases for a specific supported by a DB unit.
- Whenever test cases are written, then execute these test cases using a command discussed as follows.
- After execution, the result comes in the form of the pass and fail.
- If a result is a pass, then the actual result is equal to the expected result. If the status failed, then the actual result is not equal to the expected result.
Test cases can be run using the following command
Go test -v -tags integrationCommands to Test DB Result for both cases either pass or fail shown below-
- If expected and actual value matches to each other. Then a test case will move, and the following result will be visible.
- If an expected result and actual result is not equivalent to each other, then the test case will fail, and the effect will occur.
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What are the best tools?
Some of the tools mentioned below that help to execute these type of testing on a database.
- Benchmark
- Rider
- Db stress
- DbUnit
- DB Test Driven
- HammerDB
- JdbcSlim
- JDBC (Java DataBase Delta Testing)
- Nbi
- NoSQLMap
- NoSQLUnit
- ruby-plsql-spec
- SeLite
- sqlmap
- tSQLt
- Tsung
- utPLSQL
Concluding
Database testing assures security and reliability of an application. We have understood that keeping the privacy of user's data is of prime importance. Therefore it is essential to carry out it to ensure data integrity, consistency, etc. are maintained and while performing its testing one must remember the 'why' and 'how' criteria.A Compressive Approach
Building a highly-scalable, and cost-effective software helps Enterprises to Increase business efficiency. For making imperative decisions based on automation and testing, we advise taking the following steps- Get an Insight about "Smoke Testing“ and "Shift Left Testing"
- Discover the need for "Big Data Testing”
- To Learn more about XenonStack “Get in Touch" with us.
