Wednesday April 27, 2016
Database is undoubtedly a critical element for every application. Even companies such as e-commerce service provider, pharmaceutical establishments as well as banking and insurance corporations utilize large sets of database and thus the need to have an indestructible and a coherent database system is essential which can be achieved through dedicated database testing in place.
With increasing data complexities, heterogeneous environments and data sizes in terabytes, it is really difficult to form a testing strategy based on manual testing. In these cases, test automation can help perform data validation, schema verification, database health check, security checks, etc. It helps in performing the right data checks in optimum time-frame and budget. However, to ensure there are no database crashes, failovers, broken insertions or deletions, one needs to create a sound database test automation strategy. This can be achieved by following the right steps.
Here’s are the 8 steps to performing automation database testing accurately:
1. Identification of Scope
The first thing is to identify what needs to be covered in database testing, what can be excluded, and which all data sets are impacting the application performance most. Based on this identification, aspects like data verification, schema verification, health checks, database guideline checks, security checks and a lot more can be clubbed together in the scope of testing.
2. Test Script Preparation
The next step is to prepare the test scripts which requires you to grab needful SQL queries to be executed, identify conditional flows and prepare automation scripts. While preparing test scripts, always keep a version of test scripts in mind for future utilization and also make them code maintenance-friendly right at the initial stage.
3. Test Case Identification
This step is to identify the priority test cases to carry out data checks with query to query or query to previously stored data. It requires you to create needful batches in case there are huge data sets. While identifying these test cases, make sure they are flexible enough so that for a few test cases, testing can be performed at an abstract level while others can be done till row level comparison.
In this step, you need to execute scripts that you have prepared or tweak them to perform as expected. You can add needful conditional flows, loop around to use the same case for multiple sets of data and make them more robust. While execution, also make sure you consider the test environment to obtain accurate test results.
Once the execution is over, monitor the outcome of the execution. You can dig into the detailed analytics and generate reports for managers and developers to take necessary actions. Further, you can also make a provision to store the reports in the format of choice for further evaluation and analytics.
6. Post Execution Monitoring
Post the execution and reporting, monitor the test results to identify the trends and make necessary tweaks and fixes to the test scripts. This can be performed as a continuous activity to conclude on some particular pattern of the defect.
7. Test Script Reuse
Post the unit execution, you can further reuse the test script for regression test execution. In this stage, you can actually use the tweaked code. If possible, you can also put test case for scheduled execution to give you the flexibility to run it in the desired time.
8. Cross-checking with UI Testing Report
Lastly, cross-check the database automation testing results with UI testing and affirm the findings made by two separate channels to take further actions.
Database testing is not about checking data of the applications that developers have prepared. It is something which can ensure that your applications perform functions faster and better. With people, processes and technologies, there are several direct and indirect benefits of database testing.
Besides the above points, understanding organization’s strategic and business objectives as well as an investment in the right automation tool is crucial before you jumpstart database testing automation to reap the benefits.
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