What is volume testing?
Volume testing is a form of performance testing which determines the performance levels of the server throughput and response time when concurrent users, as well as large data load from the database, are put onto the system/application under tests.
Volume testing is defined as a type of software testing where the software is subjected to a huge volume of data. It is also referred to as flood testing.
Volume testing is done to analyze the system performance by increasing the volume of data in the database.The impact on response time and system behavior can be studied when exposed to a high volume of data.
Benefits of Volume Testing
- By identifying load issues, a lot of money can be saved which otherwise will be spent on application maintenance.
- It helps in a quicker start for scalability plans
- Early identification of bottlenecks
- It assures your system is now capable of real-world usage
The objective of performing the volume testing is to
>>Check system performance with increasing volumes of data in the database.
>>To identify the problem that are likely to occur with large amount of data.
>>To figure out the point at which the stability of the system degrades.
>>Volume Testing will help to identify the capacity of the system or application-normal and heavy volume.
How to do Volume Testing:
In volume testing,following things need to be tested.
- Test to check if there is any data loss.
- Check the system's response time.
- Check if the data is stored correctly or not.
- Verify if the data is overwritten without any notification.
- Check for warning and error messages,whether it comes at all for volume problems.
- Check whether high volume data affects the speed of processing.
- Does system have the necessary memory resources.
- Does volume test executed on the whole system.
- Is there any risk of data volume is greater than specified.
- Is there any guarantee that no larger date volume will occur than specified.
Challenges in Volume Testing
- Fragmentation of memory difficult to generate.
- Dynamic generation of keys.
- Relational Integrity of generated data.