Software with high quality, reliability and high-end features goes through various processes before the release. Testing the software is one of the major phase that helps to release an effective and efficient software with minimum amount of bugs (with free of all possible errors). During the software testing phase, there are many to consider; such as test data, test environment, test cases, comparison of expected & actual results and etc. Even though there are many areas to be considered, having reliable test data which is closer enough to the live data (production data) is essential; failure to do so, may change the direction of testing phase and the low quality software would have been resulted. So, test data management is the must to warrant the reliable and dependable software product. Let's discuss on the different stages of test data management process.
Learn the Requirements for Test Data
Requirements of test data may depend on the product and nature of the required testing etc. Basically, the requirement analysis is about determining what are the required types of test data, data source requirements, amount of required test data etc.
Make the Strategy to Apply the Test Data
The test data strategy thoroughly depends on the analysis conducted regarding the test data requirements. Collecting the data that meets the testing requirements is the problem; the solution for that issue can be found at this stage. During the requirement phase the necessary subset of the production data would have been identified along with the data interdependency and other relationship among data; based on such understanding, the strategy can be developed whether to use automated tools or scripts to gather the data from production data; also it has to be noted on the strategy that the gathered data should have to be privatized (masking and other methodologies can be suggested at the strategy) and prioritized before using it (should be done before sending out from the production environment), to avoid any privacy concerns/issues.
Also, this phase is about planning optimum solutions such as preparing storage for the test data; measuring the cost, effort & time for the software testing project and getting necessary steps to make the resources ready; figure out what are the possible problems that can arise during the software testing phase and more.
Generating the Test Data
Previous phases are about determining and taking decisions on test data management. Here, the decisions are to be actions. According to the test data practicing strategy the test data should be gathered or generated. Identifying and collecting required test data from different databases, storing the data in local storage medias would be the major tasks to handle in this phase. However, without spending more effort and time on locating and collecting test data(by manually) from deviated databases (different database types, different locations where databases are placed etc), software solution can be used to generate the bulk amount of test data that meet the test data requirements. If automated software solution is used, then the time, effort and cost to be spend on test data management process would reduce drastically. Specially since the automated test data generation tools can be customized to deliver only the valid test data without any deviations from the test data requirements; if human users are going to create test data that matches the initial test requirements, there may be possibilities for low reliability compare to the data generated by automation tool.
Learn the Requirements for Test Data
Requirements of test data may depend on the product and nature of the required testing etc. Basically, the requirement analysis is about determining what are the required types of test data, data source requirements, amount of required test data etc.
Make the Strategy to Apply the Test Data
The test data strategy thoroughly depends on the analysis conducted regarding the test data requirements. Collecting the data that meets the testing requirements is the problem; the solution for that issue can be found at this stage. During the requirement phase the necessary subset of the production data would have been identified along with the data interdependency and other relationship among data; based on such understanding, the strategy can be developed whether to use automated tools or scripts to gather the data from production data; also it has to be noted on the strategy that the gathered data should have to be privatized (masking and other methodologies can be suggested at the strategy) and prioritized before using it (should be done before sending out from the production environment), to avoid any privacy concerns/issues.
Also, this phase is about planning optimum solutions such as preparing storage for the test data; measuring the cost, effort & time for the software testing project and getting necessary steps to make the resources ready; figure out what are the possible problems that can arise during the software testing phase and more.
Generating the Test Data
Previous phases are about determining and taking decisions on test data management. Here, the decisions are to be actions. According to the test data practicing strategy the test data should be gathered or generated. Identifying and collecting required test data from different databases, storing the data in local storage medias would be the major tasks to handle in this phase. However, without spending more effort and time on locating and collecting test data(by manually) from deviated databases (different database types, different locations where databases are placed etc), software solution can be used to generate the bulk amount of test data that meet the test data requirements. If automated software solution is used, then the time, effort and cost to be spend on test data management process would reduce drastically. Specially since the automated test data generation tools can be customized to deliver only the valid test data without any deviations from the test data requirements; if human users are going to create test data that matches the initial test requirements, there may be possibilities for low reliability compare to the data generated by automation tool.