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The ProClarity Standard client is the completely thin-client version of ProClarity that is part of the ProClarity Analytics Server It is a series of pages that allow nearly all the same functionality as the Desktop Professional client while being completely web-based As a web-based application, the Standard client does have significant differences in how certain actions are performed, but most of the functionality of the Professional client is available In a few rare cases, the Standard client even includes functionality not found in the Professional client, as will be pointed out in the following sections Perhaps one of the most significant differences between the Standard and Professional clients is that the Standard client does not include any drag-and-drop capabilities The Standard client is not AJAX-enabled, but instead operates like a series of traditional web pages Users start by selecting the library they want to view, then a book in that library, and finally a view in the particular book The view is rendered and looks as close to the view saved in the Professional client as possible; there are minor changes to the display because of limitations of a web client versus a rich Windows application Another major difference between the Standard and Professional clients is the lack of pop-up menus when right-clicking on a chart or grid The menu that appears when a user right-clicks is the standard Internet Explorer menu and has nothing to do with the underlying page being viewed Internet Explorer does not see the ProClarity view as different from any other web page it might render, so the menu does not contain any ProClarity-specific options Instead, items are most often selected by left-clicking on them and then selecting options in one of the Standard client tabs, some of which mimic the Professional client s Setup Panel while others include sorting, filtering, and so forth The differences between Standard and Professional and the ways to analyze data using the Standard client is discussed in the next sections.

The Average function will return the average value over the numeric report_variable provided The report_variable can be either a universe object variable or a user-defined variable

number Average (number [report_variable]; [INCLUDEEMPTY])

Figure 4.3 Our test will create a MockWebService to record messages that LogAnalyzer will send. It will then assert against the MockWebService.

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[INCLUDEEMPTY] is an optional input parameter By default, the Average function ignores nulls when calculating the average If the [INCLUDEEMPTY] parameter is provided, the Average function will take nulls into consideration Examples: Average([Revenue]) will return 44,66667 when the [Revenue] measure contains the values (45000; 37000; 52000; <Null>) By default, the Average function ignores nulls when calculating the average, so the calculation based on the preceding value is (45,000 + 37,000 + 52,000/3 Average([Revenue];IncludeEmpty) will return 33,500 when the [Revenue] measure contains the values (45000; 37000; 52000; <Null>) When the optional IncludeEmpty parameter is provided, the average calculation becomes (45,000 + 37,000 + 52,000 + 0)/4

The Count function will return the count of items in a report_variable provided The report_ variable can be either a universe object variable or a user-defined variable

When working with charts and grids, it s important to remember that clicking on the member names does not drill down as it does in the Professional client Clicking on

Creating and using a mock object is much like using a stub, except that a mock will do a little more than a stub: it will save the history of communication, which will later be verified. Let s add a new requirement to our LogAnalyzer class. This time, it will have to interact with an external web service that will receive an error message whenever the LogAnalyzer encounters a filename whose length is too short. Unfortunately, the web service we d like to test against is still not fully functional, and even if it were, it would take too long to use it as part of our tests. Because of that, we ll refactor our design and create a new interface for which we can later create a mock object. The interface will have the methods we ll need to call on our web service, and nothing else. Figure 4.3 shows how our MockWebService will fit into the test. First off, let s extract a simple interface that we can use in our code under test, instead of talking directly to the web service:

integer Count(any_datatype [report_variable]; [INCLUDEEMPTY]; [DISTINCT/ALL])

[INCLUDEEMPTY] is an optional input parameter By default, the Count function ignores nulls when counting the items in the report_variable If the [INCLUDEEMPTY] parameter is provided, the Count function will take nulls into consideration [DISTINCT/ALL] is an optional input parameter By default, the Count function only counts distinct items in the report_variable If the [ALL] parameter is provided, the Count function will count all items in the report_variable whether they are distinct or not Examples: Given the following sample report:

22:

public interface IWebService { void LogError(string message); }

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