Database Testing – Properties of a Good Test Data and Test Data Preparation Techniques

A couple of months ago, I wrote about database testing strategies. It covered the aspect that is entirely related to the execution of test cases. It was all about black-box testing of a database. There is another important aspect of DB testing activity which we will cover in this article.

As a tester, you have to test the ‘Examination Results’ module of the website of a university. Consider the whole application has been integrated and it is in ‘Ready for Testing’ state. ‘Examination Module’ is linked with ‘Registration’, ‘Courses’ and ‘Finance’ modules. Assume that you have adequate information of the application and you created a comprehensive list of test scenarios. Now you have to design, document and execute these test cases. In ‘Actions/Steps’ section of the test cases, you must mention the acceptable data as input for the test. The data mentioned in test cases must be selected properly. The accuracy of ‘Actual Results’ column of TC Document is primarily dependent upon the test data. So, step to prepare the input test data is significantly important. Thus, here is my rundown on ”DB Testing – Test Data Preparation Strategies”.

Properties of Test Data:

The test data should be selected precisely and it must possess the following four qualities:

1. Realistic: By realistic, it means the data should be accurate in the context of real life e.g. in order to test ‘Age’ field, all the values should be positive and 18 or above. It is quite obvious that the candidates for an admission in the university are usually 18 years old (this might be defined in requirements).

2. Practically valid: This is similar to realistic but not the same. This property is more related to the business logic of AUT e.g. value 60 is realistic in age field but practically invalid for a candidate of Graduation or even Masters Programs. In this case, a valid range would be 18-25 years (this might be defined in requirements).

3. Versatile to cover scenarios: There may be several subsequent conditions in a single scenario, so choose the data shrewdly to cover maximum aspects of a single scenario with minimum set of data, e.g. while creating test data for result module, do not only consider the case of regular students who are smoothly completing their program. Give attention to the students who are repeating the same course and belong to different semesters or even different programs. The data set may look like this:

Sr#	Student_ID	Program_ID	Course_ID	Grade
1	BCS-Fall2011-Morning-01	BCS-F11	CS-401	A
2	BCS-Spring2011-Evening-14	BCS-S11	CS-401	B+
3	MIT-Fall2010-Afternoon-09	MIT-F10	CS-401	A-
…	…	…	…	…

There might be several other interesting and tricky sub-conditions. E.g. the limitation of years to complete a degree program, passing a prerequisite course for registering a course, maximum no. of courses a student may enroll in a single semester etc. etc. Make sure to cover all these scenarios wisely with finite set of data.

4. Exceptional data (if applicable/required): There may be certain exceptional scenarios that are less frequent but demand high importance when occur, e.g. disabled students related issues.

Tweet