In today’s era, most of the enterprises and QA managers are adopting Automation testing to speed up the test time to market and thereby enhancing the software quality. Automated testing tools are capable of executing the test cases, reporting the outcomes and comparison of results with the previous test runs. Tests that are once carried out with these tools can be run repeatedly. But one thing to be considered is that all the test automation projects do not deliver expected ROI and success. The reason could be utilization of wrong test practices. The testers implement the test automation tools even if they are not aware of the right procedures which reduces the effectiveness of test automation.

A robust mobile test automation approach can help the companies to test quickly and effectively. At the same time, a poorly designed approach can seriously put a negative impact on the productivity and ROI. So, maximum investments can be made from test automation and also test automation effectiveness, ROI can be increased if the following strategies are kept in mind from the very beginning:

1. Selection of the right test cases to automate: Mobile application testing involves the test cases that get repeated across different platforms, devices and networks. For successful test automation, identification of the right test cases to automate is essential. Some of the ideal cases are:

i) Cross platform test cases

ii) Test cases that use large amount of input data

iii) Repetitive test cases

iv) Test cases that deal with complex business logic

v) Test cases that require multiple users

2. Prioritization of the tasks: After the selection of the tests that are to be automated, you can go for the prioritization and division of the tests. With the help of this, the outcome can be estimated that one wishes to achieve through test automation and accordingly the actions can be planned. You can set your priorities in terms of the complication of the tasks. Then the tasks can be divided in an efficient manner in order to meet the specified time limit. Every team member possess different skills and experience. The tests that are beneficial for automation should be identified first.

3. Preparation of the test cases: Before stating test automation, you should create test scenarios and test cases. This will let you know the execution time and defects in advance. This will bring more scenarios into effect if you have missed and helps you understand the workflow of the application.

4. Test process: Before jumping into test automation, the process of execution should be prepared so that the suitable resources can be allocated to discover probable risks beforehand. Identifying the test process will bring the best methods to perform the task effectively and efficiently.

5. Create stable test: The test scripts that are usually created get changed with respect to the UI changes in terms of functional aspects. Due to this, such test scripts may not work well for the successive versions of the application. Therefore, the tests should be created in a way that they remain unaffected by the UI changes. It would eliminate the need for making frequent changes and keep the automated tests work smoothly.

6. Do not automate everything: The automation testers often try to automate each and everything for more application coverage that results in millions of test combinations. If all these test cases are to be executed, it would result in increased efforts and costs. Sometimes, for certain complex scenarios, manual testing is required as automation testing cannot bring out expected results in such cases.

7. Review tests: The test data and test cases should be reviewed after certain intervals to keep a check on their validity for tests. Reviewing helps to eliminate the old and outdated tests that are irrelevant to the current test requirements which ultimately helps in reducing the costs and efforts.

8. Quality of test data: Test data quality plays a major role in the process of test automation. For successful automation quality test data is of prime importance. Quality data set refers to categorization of data sets into valid data, invalid data or boundary condition etc. One needs to make sure that the data being used for testing is not obsolete and is updated from time-to-time for accuracy.

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