The "Data" in Data-Driven Silk Testing


Datatect 1.6 has a simple and clean user interface. As shown in Fig 1., the main screen is composed of 9 tabs. The first three are used to create Datatect Specifications (more on this later) and the remaining 6 tabs are used to create Datatect Definitions.

Basically, a definition is a user-defined or built-in data type with one of the following characteristics: Characters (proper names, addresses, phone numbers, etc.), Numbers (Integers, decimals), Dates/Times, Lists (user-defined), and Multi-Fields (combinations of built-in and user-defined types).

Borrowing from my earlier username/password example, we created a Datatect 'Username' defintion using the Character tab. The screen shot in Fig. 2 shows the selections we made in this dialog for user names.

For our passwords, we first created two more definitions: a Number (unique random), and another Character (alphanumeric). We then combined these two definitions using a Datatect Multi-Field definition so that our passwords would contain a mix of characters and numbers and, most importantly, they would be unique.

After creating our definitions, the next step was to create a Datatect SilkTest specification to output the results of our data generation. This is where Datatect really shines for SilkTest users. Due to various confidentiality and trademark issues regarding DoDots intellectual property, I will illustrate this feature using the default Green Mountain Outpost (GMO) specification provided by Datatect

In Fig. 3, you can see the SilkTest output specification for the GMO application. On the left side, highlighted in blue, is the field "BillName".

To the right, you will find the various parameters you set according to the type of GUI control (Textfield, Checkbox, Combobox, ListBox, PopupList, and RadioList) under test, the 4Test method required to input the data into the GUI control, the SilkTest data type (String, Real, Number, Integer, Boolean, Enumeration, Time, and Date), and the Data Source/Value (Datatect definition).

In the lower left, Datatect provides a preview (in bold) of exactly how it will generate the 4Test code to input data for the RECORD variable "BillName". Not only will Datatect generate the data text file, perfectly formatted for use in any SilkTest data-driven test case but it will also generate, if you choose, all of the 4Test code required to process each field in the data file. You simply click the Generate button in the Silk tab of Datatect, provide a name for the data output file and then click the checkbox labeled ‘Generate Silk 4Test Code’ and Datatect does the rest. It creates a ".dat" data file and uses the same name to create a corresponding ".t" script containing the 4Test code.

Before we purchased Datatect, I tested the default Datatect GMO specification to generate the evaluation limit of 25 records and the 4Test code needed to create customer orders on the Segue GMO website. I needed to add a total of 5 lines of 4Test code to the 4Test script generated by Datatect in order to compile and run the script. Datatect created the 4Test RECORD structure and all the fields, the file handle and file processing code, the testcase, the while loop used to parse each record in the data file, various comment blocks, and almost all the 4Test code needed to input each data value into their respective fields in the various GMO forms found on Segue’s website.

The Results
Using SilkTest and Datatect, the QA team was able to create dozens of data files (containing thousands of records) based on our established equivalency classes for testing the DoDots application. There were also two rather unexpected side effects from our data generation efforts.

One of the first things I noticed was

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