Importance of Synthetic Data in Test Development Processes

Data mining is a powerful tool used by programmers to extract the needed information from large amounts of unstructured data. In the past, programmers would have to deal with manually the extract the data from the data sets. In fact, programmers would need to do this manually to extract the required information. However, today thanks to Synthetic Data, things have changed for the better.

Synthetic Data is one of the components of the Data Quality suite, developed by Oracle. This suite provides high levels of data security and quality assurance along with a variety of reporting and graphing tools. It also comes with support for several language levels and varying databases like Oracle, SQL Server, MSSQL, and much more. Synthetic Data provides the ability to easily deal with unstructured data. One of the most useful features of Synthetic Data is that it can be used as a normal input/output device and can be used again as a variable. Synthetic Data’s flexibility is the reason why it has become popular amongst software development companies.

The most common problem associated with manual data mining is the time it takes to analyze the data sets. It is also time consuming to keep track of the changes made to the data. Sometimes Synthetic Data may use more than one data source and reconstruct the data set from these sources. This can increase the complexity of the resulting data sets. In such cases, it would be impractical to keep track of all the changes that Synthetic Data has made.

Synthetic Data minimizes the problems associated with manual analysis of data sets. The test cases generated using Synthetic Data will be consistent with the test cases generated using other tools. Furthermore, Synthetic Data is fast. It can analyze large amounts of data in a short time frame without consuming a lot of resources.

Some of the benefits of using synthetic data in a data driven application include:

– Test cases can be executed repeatedly without fear of rejection.

– Good test plans can be developed quickly.

– The test plan can be reused for different types of data sets with little effort.

– Rejection of a test case can be easily handled.

– Changes in test execution can be tracked easily.

– Bugs in the underlying software or hardware can be detected before the application is deployed.

There are many other advantages that make using synthetic data in a data driven application much better than using it for manual analysis. Synthetic Data allows quick feedback even to the smallest changes in the test cases or software. Moreover, the user can easily change test variables during the testing procedure, which can eliminate the risk of data mining bugs.

While a SyntheticData user can use a single data source to generate the test cases, users may also use a combination of data sources. When testing a particular software program, sometimes it may not be feasible to generate a test case from just one source. In such situations, users can choose to combine two or more test cases into a single data set. Similarly, users can choose to generate their test cases from a mixture of different sources. The test execution can then be divided into smaller pieces and the tedious task of data mining can be bypassed.

However, synthetic data presents certain difficulties for test automation. Sometimes it becomes difficult to locate the right data sets and the right test scripts. Also, the generated test cases have to be compatible with the software being tested.

As synthetic data is purely virtual data set, the results cannot be compared with real data or with any other internal or external test data. Moreover, synthetic data presents certain drawbacks such as overfitting or underfitting of the model. This means that sometimes users end up getting unrealistic final results. The overall complexity of the software application increases due to the additional parameters that are specified by the developers.

Synthetic data is not recommended for automated testing. It can be difficult for the testers to set the appropriate criteria for the application and set the appropriate time for each testing procedure. In addition, manual testing still needs to be performed in order to check the expected output of the software application. Therefore, in case of any difficulty, manual testing is still the best option.

In most of the recent software development models, the developers of the software tools to create test cases and then use them to generate synthetic data. Such procedures make the application simple enough for any user to use easily. One important factor about such procedures is that the user can specify which data sets should be included in the test case and which should be excluded. In addition, the user can also specify the number of categories that should be tested and how frequently the results should be verified. In most of the cases, such procedures make it easy for test developers to generate and maintain test data sets on-demand.