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Table 8-3: Partial Data for Grammar Model Open table as spreadsheet Invalid address Empty Not empty Length == 0 Length > 0
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A test generator could use the model along with a database containing valid and invalid e-mail addresses, random subject lines, and message bodies to generate 10,000 unique e-mail messages. Note that yet another tester could easily create another grammar model to create random strings for the subject and message body. A Modeling Success Our test team used model-based testing on several new features from the Windows Vista operating system time frame and found that very extensive coverage could be achieved with a small amount of work. One of the hardest problems was creating a model that accurately depicts the system in question.
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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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term monkey test comes from the infinite monkey theorem, which states that a thousand monkeys at a thousand typewriters will eventually type the complete works of William Shakespeare.
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that this regular expression also would find instances of Alen. This is simple to fix, but outside the scope of this book.
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As exciting as modeling sounds, model-based testing is not the same thing as modeling. Many testers, however, fail to make this distinction. A model can be a very powerful tool for the design/development team, even if it never generates a test case. In this section, we examine two examples of modeling power that do not directly affect test cases. One example demonstrates how to model uncertainty or risk, and the other provides a method for testers to use to ensure that their models behave as they should.
Bayesian Graphical Modeling
Bayesian Graphical Modeling (BGM) [3] is notably different from the other modeling approaches used at Microsoft. [4] The goal of BGM analysis is to measure and reduce the uncertainty of testing. It is a method of modeling for risk analysis. Every tester comes to an application to test with some measure of confidence in the code she tests. Many factors inform this confidence (reputation of the developer, complexity of the code, number of dependencies on other binaries taken, time and cost of testing, and other factors). If every tester and every application starts out with the same presumption, we have a good starting point for measuring and reducing uncertainty.
If a tester presumes the quality of the code tested is good (especially if there are strong internal development controls in place), a proper starting point for all testers is to answer this question: How confident am I that there are no defects in this code In a BGM, the tester specifies his confidence for all the components he will test and fully describes his reasoning in a model. The BGM takes into account that uncertainty and risk affect dependent components as well. Testers then run tests, and as they find bugs, they update the BGM. The model and tests form a feedback loop of updates throughout the product cycle. An example of a BGM is shown in Figure 8-12.
Figure 8-12: Product Quality Assessment Using a Bayesian Graphical Model. We testers will never enjoy complete certainty in our job, but we can measure and take steps to reduce the uncertainty of our task. At some point, we have to ship, and it's nice to know when the odds of success are in our favor.
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