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Unit Testing Algorithms
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What a curious feeling! said Alice. I must be shutting up like a telescope! And so it was indeed: she was now only 10 inches high, and her face brightened at the thought that she was now the right size for going through the little door into that lovely garden.1
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One of the main messages in this book has been test smarter, not harder that is, write fewer tests that each cover more code. If done using the techniques we ve described, you can actually improve your effective test coverage while having fewer test cases (but comparatively more test scenarios, or inputs and their expected outputs, for each test case).
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Lewis Carroll, Alice s Adventures in Wonderland (1865), 1.
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CHAPTER 12 UNIT TESTING ALGORITHMS
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This approach should work well for the majority of business code out there. However, almost every project has a core element: an engine or state machine that s central to the rest of the system; core business logic that would potentially cost the company millions of dollars if it went wrong; mission critical code; intricate algorithms; and so on. For this type of code, it makes sense to apply a more comprehensive set of tests, which don t just test the outputs of a function, but zoom in to a sub-atomic level and examine the function s internal operations, not leaving any code flow or state transition to chance or assumption. Thankfully, DDT can help here too: you can draw out a design diagram (or set of diagrams) and then use the standard DDT pattern to identify test cases and test scenarios.
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Caution Algorithmic unit tests are even finer-grained than normal unit tests i.e., they go sub-atomic. Though algorithmic unit tests are well suited to the types of code we ve just described, we wouldn t recommend them for everyday code you d be there for most of this decade and the next, writing intricate tests for code that almost certainly doesn t require that level of testing.
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In this chapter we ll demonstrate how to apply DDT to create algorithmic unit tests. We ll illustrate the process with UML activity diagrams, but the same principles should hold true for other types of dynamic diagrams (that is, diagrams that describe a sequence of events or state changes rather than a static model such as a class diagram). The unit testing techniques described in this chapter build on the ICONIX Process for Algorithms roadmap, which originated with Doug s work with the Large Synoptic Survey Telescope image processing pipelines, and is described in the upcoming book ICONIX Process Roadmaps (Fingerpress, September 2010).
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Top Ten Algorithm Testing To-Do s
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Following is a list of the top ten to-do items for algorithm testing: 10. Start with a controller from the conceptual design. 9. 8. 7. 6. 5. 4. 3. 2. 1. Expand the controller into an algorithm design using activity diagrams, statechart diagrams, etc. Tie the diagram loosely to your domain model. Split up decision nodes that look like they re doing more than one check. Create a test case for each node (Activity and Decision nodes). Define test scenarios for each test case, specifying a range of inputs and expected outputs. Create input data from a variety of sources, depending on the algorithm; e.g., use fuzzing techniques. Assign the logic flow into individual methods and assign them to classes. Write white box unit tests. Apply DDT to other types of design diagrams.
CHAPTER 12 UNIT TESTING ALGORITHMS
We ll structure the remainder of the chapter around this top-ten list.
10. Start with a Controller from the Conceptual Design
Think back to Part 2 for a moment you ve created a slightly higher-level conceptual design (see 6), and then evolved this into a low-level detailed design ( 5), with a gritty realworld sequence diagram drawn out for each use case, exploring all the ins and outs, the sunny day and rainy day scenarios. From this you ve identified unit tests that weren t already covered by controller tests, or which looked like they would benefit from a more zoomed-in form of test, operating on an individual function instead of a group of functions. In this chapter we ll create an algorithmic design for a single controller from the Use Address use case from 7. To recap, here s the full narrative use case. We ve shown the part of the Basic Course that we ll focus on in this chapter in bold:
BASIC COURSE: The user types an address using all address fields on the Quick Search window. The system enables the Locate button as soon as an entry was made in either one of these fields: City, State, Postal, Country. The user clicks Locate . The system geocodes the location based on the level of detail provided by the user and stores any candidates in an Address Candidate Collection. If a single candidate is found or exactly one of the multiple candidates has a 100% match rate, the System sets the AOI based on this Address Candidate. ALTERNATE COURSES: The user clicks Clear : Entries in any fields will be cleared. Multiple valid candidates found: The System displays an Address Candidate widget with a list of potential candidates to choose from. The user selects an Address Candidate. No candidates were found: The system displays a message: "Location not found".
Figure 12 1 shows the robustness diagram (i.e., conceptual design) for this use case we ve circled the part that we ll turn into an activity diagram.
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