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Once this was achieved by properly defining all possible inputs, transitions, and outputs, we found it very easy to accurately define an expected result from any possible set of parameters. The model in essence becomes the ideal representation of the system you are testing and any deviation should be readily apparently. We found model-based testing very robust because we could apply it to test areas ranging from APIs to UIs. This allowed us to do away with manual testing completely when the models were implemented. In particular with our UI, this saved us countless hours of manually verifying its functionality for all possible actions (button clicks, text inputs, and so forth). When properly scaled, our model-based tests could provide coverage ranging from quick verification tests to full-scale functional tests. This was done by paring down our list of inputs, transitions, and outputs to the subset that we were interested in. It would have been very difficult to achieve such a wide range of coverage if we relied on developing specific test cases one by one. One of our successful implementations of modeling involved testing a new API that shipped out to developers in Windows Vista. Rather than write test applications that used the API or script a series of possible scenarios, we used modeling to generate a state machine for all possible function calls that a developer could make. This means for a given function call with given parameters, we could determine how all other subsequent function calls would react. We realized the true value of our framework in testing whether the API reacted correctly to different options/parameters/completion schemes without writing one-off applications for each interesting scenario. Our test footprint was much smaller with the model while achieving more coverage compared with our older approaches. Jim Liu, SDET, Windows Networking

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The Internet is host to a significant number of tutorials and guides on how to use various features of Ruby and its libraries. Often there are multiple tutorials on how to do the same thing in different ways, and tutorials can appear quickly after libraries are released. This is why it s worth subscribing to a few Ruby blogs so that you can learn about the latest action as it happens. However, in this section are links to a number of useful tutorials and guides that have already proven useful, including installing and setting up Ruby, Ruby on Rails, and testing, as well as tutorials on a few other miscellaneous techniques and on Ruby in general.

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The #define and #undef directives are used to define and undefine conditional compilation symbols. The #if directive, described in the next section, uses these symbols to control conditional compilation. The following example demonstrates the use of the #define and #undef directives:

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8 Feb 2012 ... Javadocs · Scenarios ... format; Links. also known as: UCC/ EAN - 128 , GS1 - 128 ... EAN - 128 is based on the Code 128 symbology. The height ...
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The formula for computing loan payments is complicated and fairly expensive. Putting the information into a table instead of computing it each time would probably be cheaper. How big would the table be The widest-ranging variable is loanAmount. The variable interestRate might range from 5 percent through 20 percent by quarter points, but that s only 61 distinct rates. months might range from 12 through 72, but that s only 61 distinct periods. loanAmount could conceivably range from

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#<FasterCSV::Table mode:col_or_row row_count:4> Rather than importing an array of arrays, when you use headers it creates a FasterCSV::Table object containing a FasterCSV::Row object for each row. FasterCSV::Table provides a number of useful methods (these examples assume csv contains a FasterCSV::Table object, as in the previous example) as follows: csv.to_s returns a string containing the entire table in CSV format. You can use this to rewrite the data back to file. csv.to_a returns the table as an array of arrays, as FasterCSV.read would if you hadn t used the :headers option. csv << can be used to push a new row onto the end of the table (for example, csv << ['Chris', 26, 'male', 'Los Angeles']). csv.headers returns an array of the header row. csv.delete('Name') removes the Name column from every row. csv.delete(n) deletes the nth row. csv[n] returns the nth row. csv.each iterates through each row using a code block.

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This test generates the following results on my system:

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