Synthetic Test Data (STD) is a collection of performance and test results that are used in enterprise resource planning (ERP) applications. This data is designed to provide a more complete picture of the software and hardware that has been deployed in an organization. It is obtained from the current system in place and then analyzed to provide an indication of future performances. In many ways, STDs represent a statistical normal distribution of condition for the system or application.
It is important to remember that STDs obtained from current systems will not be representative of future conditions. This is because most of the tests performed today involve complex measurements that do not reflect typical usage patterns. Such measurements are not captured by the average user in a typical usage environment. Hence, the results obtained from these tests will be very inaccurate in nature.
While using synthetic test data to improve the quality of testing results, it is essential to use test data driven automation tools that can take full control over the entire process. The tool should enable one to generate accurate metrics and dashboard reports. In addition to generating reliable metrics and dashboard reports, the tool should also allow the user to directly access performance feedback and user defined thresholds for testing. With the help of such tools, one can ensure that the amount of testing effort required to bring a product to market is reduced.
There are several problems that can arise when the testing data obtained from a third party application is merged with synthetic test data. Most commonly, problems arise due to the inability of the user to interpret and compare the data from both the STDs and traditional databases. This leads to a situation where in some cases, the data provided by the third party application is more accurate than the data collected from the on-site testing lab. Similarly, there can be cases where the time taken for STDs to generate valid data and the number of system tests performed is more than what can be justified by the end user. Such a situation often leads to increased costs and time delays, as users need to wait for the data to be validated. Such a scenario can be avoided if the on-site testing lab has access to real time data from various STDs.
Another problem arises due to poor STI planning and management. Some testing tools generate synthetictestdata based on assumptions and hence end up being quite unrealistic and erroneous. This leads to unexpected results and delays in deliverables. To remedy the problem, the best option is to validate the STIs and make sure they are based on realistic assumptions and that they do not invalidate any of the on-site database tests.
Apart from these typical issues, there can be certain issues that cannot be addressed by using on-site tools for the purpose of STI validation. For instance, the type of database used for STDs may no longer be applicable or compatible with the type of database used for normalizing data for performance testing. Similarly, the types of queries used for testing may no longer be applicable. This means that new and different parameters to be tested would have to be generated from scratch. This can prove to be a tedious task, if a test lab has no access to reliable sources to generate these new parameters.
Another important issue is data redundancy. Since the goal of on-site testing tools for STDs is to minimize database corruption, one would expect the most database files to be checked during the process. However, many tools, especially those based on Oracle, have a tendency to check database files one at a time, leading to severe data duplication issues. The worst case scenario is when duplicate data sets are produced during the same test.
Choosing the right testing tools for STI validation can help you cut your costs and improve productivity. A reliable and effective tool will enable you to check database files for inconsistencies and errors while also ensuring that the results presented to the developers are accurate and up to date. If you know the types of problems to look out for, then you can choose a synthetic test data application that will provide you with the best results possible in performance testing.