In this article, we will learn volume testing, its objectives and characteristics, attributes of volume testing, how it differs from load testing, and the challenges in volume testing, some useful guidelines for volume testing, the benefits and disadvantages of volume testing and some tools for and real-life examples of volume testing.
Volume Testing is a category of software testing, performed to test a software application with a huge volume of data. The volume of data used in volume testing varies from the size of a database to the size of an interface file.
When a software application is being tested with a database size, the database is extended to that size, then the performance of the application is evaluated. When an application requires to interact with an interface file, this could be reading or writing the file. A sample file of the required size is created, then the application is tested with that file to test the performance of the application.
In volume testing, the behavior of the software and the effect on response time, with a huge volume of data, is examined. It is also known as Flood Testing. An example of volume testing is: to test a music app behavior, when millions of its users download songs.
The performance of the software declines as the volume of the data is increased with time.
The test data is generally created by a test data generator.
While developing the software, only a small amount of data is tested.
The test data must be logically correct.
The test data is used to evaluate the performance of the software.
During volume testing, it is made sure that no data is lost so that no important information is missed.
During volume testing, the response time and behavior of the application is tested.
During volume testing, it is checked if the data is accurately stored or not, else it is restored in a proper place.
Determining the capacity of the software − Volume testing provides insights to predict the amount of data the software, under testing, can process without failing or crashing. The knowledge of the capacity of the software helps plan scalability and creating contingency plans.
Discovering errors − Volume testing helps in discovering errors when the load upon the software increases. For example, higher response time, software failure, security exploits, etc.
Response time − Volume testing helps ensure that the performance of the software is not hindered and the response time remains higher irrespective of the amount of data shared by users via the software.
Preventing data loss − Volume testing is the only possible way to ensure no data is lost due to the increasing size of the database and the increasing pressure on the software.
Minimizing operation costs by identifying issues quickly − Response time helps QA team capture warning signs of the software failure. In real-world applications, organizations can manage data loads dynamically, by increasing the amount of disc space or increasing the size of database as the amount of data reaches the specified threshold.
Designing scalability plans − Volume testing helps analyze the effects of scaling up, that is increasing the size and speed of the existing infrastructure, or scaling out, that is adding components to support the system.
Analyzing system’s performance under different data loads − Volume testing helps analyze the system’s performance under low, medium, and high data loads to ensure that the system works as expected without any issue. There is a higher risk of data loss and overwriting under high data loads. Volume testing prevents overflow and data security issues.
Response time − In volume testing, the response time of the systems is determined. Moreover, volume testing also tests whether the system responds within the specified time or not. In case the response time is larger, then the system is redesigned.
Data loss − Volume testing helps ensure there is no data loss, which might lead to some key information missing.
Data storage − Volume testing tests whether the data is correctly stored or not. In case it is not correctly store, then it is restored accordingly in a proper place.
Data overwriting − Volume testing determines if the data is overwritten without giving a prior signal to the developing team.
Increasing the size of a database − This is especially challenging in case of relational databases due to their robust structure and dozens of adjacent tables. To improve and maintain the quality of the test data, QA team collects diverse fields, both required and optional ones that include large binary files.
Understanding datatypes and the differences & connections between them − In volume testing, the QA team has to deal with a range of data, such as valid, invalid, absent, boundary, wrong, etc. To understand such datatypes, establish differences and connections between them, and to understand the way the software product reacts to these datatypes is a challenge for inexperienced testers.
Dealing with large volumes of data − In volume testing, one has to deal with large volumes of data in comparison to other types of performance testing. Managing extensive datasets demands huge workforce and also complicates automation. Moreover, developers also have to deal with the data piled up from regular testing sessions.
|Load Testing||Volume Testing|
|Emphasizes the stability of the system or software.||Emphasizes on the capacity of the system or software.|
|The system or the software is tested under normal conditions.||The system is tested under both the normal conditions and abnormal conditions.|
|Its primary focus is security issues.||Its primary focus is data storage and data loss.|
|Analyzes the performance of the software.||Analyzes the response time and behavior of the software.|
|Makes the software or system ready-to-use for the end users.|
Stop the servers and inspect all the logs.
Execute the application scenario manually before the load test is performed.
Stagger the number of users to obtain most useful results.
Balance thinking time to overcome license constraints.
Pay caution to the new build.
After the baseline is established, analyze the use case to make improvements.
Expenditure can be cut down by discovering load issues. The saved money can be otherwise used for maintaining the system or software.
It enables making scalability plans quickly.
It helps identify bottlenecks and issues at an early stage.
It ensures that the system is ready for real-world applications.
In volume testing, it is not possible to ensure precise division of memory used in realworld.
Volume testing demands a skilled database performance testing team, which would be an extra expense.
The real environment copy is difficult and complicated.
Thorough volume testing consumes a lot of time, to cover all test conditions, create and execute scripts, etc., which delays the release time of the software.
In small-scale systems, large volume of data is less likely to be engaged with the system. Thus, in such cases, volume testing becomes unnecessary.
It is not always possible to simulate an exact type of real-world data.
HammerDB − This is an open-source tool and the results obtained from this tool are used as a benchmark in the global database industry. It is a transparent rating software, and has no virtual limitations. All the top IT companies use this tool. HammerDB supports various databases such as Oracle, MYSQL, SQL Server, PostgreSQL, etc. Moreover, this tool provides expert level support, and complete and comprehensive documentation. HammerDB is compatible with both Linux and Windows platforms.
DbFit − This is also an open-source tool, that supports test-driven development. This tool is used as existing executable documentation of our system behavior. It also supports agile practices, for example test-driven development, refactoring, etc. It helps in improving the quality, design and maintainability of the system or software. It offers readable and understandable syntax, that helps in easily communicating with even non-technical people. It supports various databases such as SQL server, Oracle, etc. and also provides online documentation with examples.
JdbcSlim − In this tool, the database and the queries are easily integrated into Slim FitNesse testing. It mainly focuses on configuration, test data, and SQL commands distinctly. JdbcSlim framework supports all databases, and is used by developers, testers and business users who know SQL. It helps unsure that the requirements are written independently of the execution and are easy to understand.
NoSQL Map − This is an open-source tool, written in Python. It is designed to automatically insert outbreaks and disrupt database configurations to evaluate threats.
Increasing product or user database on a website − While loading a number of items in a shopping website database, volume testing is important to conduct to make sure that the infrastructure can handle extended data load.
A company wishes to estimate the capability of the infrastructure in supporting forecasted data volumes − Volumes testing helps in planning the processor and disc capacity, system memory, and network bandwidth required to process data securely.
Creating contingency plans − It is important to know red flags of a system’s failures. Volume testing enables the project team to discover patterns and trends in system’s behavior with the increasing data volume. This knowledge is very useful in creating contingency strategy.
Volume testing helps a system deal with extreme data loads. No other testing can replace the results or insights that volume testing can give. Volume testing is non-functional. To ensure that the system performs deals efficiently with data-volume-induced failures or crashes, a team of QA professionals must be present with a ready-to-use volume testing checklist. Volume testing determines the response time of the system, and examines its behavior under different data loads.