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You can also write code to exercise selected pieces of a program systematically. A few years ago, I developed a proprietary encryption algorithm and wrote a file-encryption program to use it. The intent of the program was to encode a file so that it could be decoded only with the right password. The encryption didn t just change the file superficially; it altered the entire contents. It was critical that the program be able to decode a file properly, since the file would be ruined otherwise.
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22. Developer Testing
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I set up a test-data generator that fully exercised the encryption and decryption parts of the program. It generated files of random characters in random sizes, from 0K through 500K. It generated passwords of random characters in random lengths from 1 through 255. For each random case, it generated two copies of the random file; encrypted one copy; reinitialized itself; decrypted the copy; and then compared each byte in the decrypted copy to the unaltered copy. If any bytes were different, the generator printed all the information I needed to reproduce the error. I weighted the test cases toward the average length of my files, 30K, which was considerably shorter than the maximum length of 500K. If I had not weighted the test cases toward a shorter length, file lengths would have been uniformly distributed between 0K and 500K. The average tested file length would have been 250K. The shorter average length meant that I could test more files, passwords, end-of-file conditions, odd file lengths, and other circumstances that might produce errors than I could have with uniformly random lengths. The results were gratifying. After running only about 100 test cases, I found two errors in the program. Both arose from special cases that might never have shown up in practice, but they were errors nonetheless, and I was glad to find them. After fixing them, I ran the program for weeks, encrypting and decrypting over 100,000 files without an error. Given the range in file contents, lengths, and passwords I tested, I could confidently assert that the program was correct. Here are the lessons from this story: Properly designed random-data generators can generate unusual combinations of test data that you wouldn t think of. Random-data generators can exercise your program more thoroughly than you can. You can refine randomly generated test cases over time so that they emphasize a realistic range of input. This concentrates testing in the areas most likely to be exercised by users, maximizing reliability in those areas. Modular design pays off during testing. I was able to pull out the encryption and decryption code and use it independently of the user-interface code, making the job of writing a test driver straightforward. You can reuse a test driver if the code it tests ever has to be changed. Once I had corrected the two early errors, I was able to start retesting immediately.
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22. Developer Testing
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Karl Wiegers reports that testing done without measuring code coverage typically exercises only about 50-60% of the code (Wiegers 2002). A coverage monitor is a tool that keeps track of the code that s exercised and the code that isn t. A coverage monitor is especially useful for systematic testing because it tells you whether a set of test cases fully exercises the code. If you run your full set of test cases and the coverage monitor indicates that some code still hasn t been executed, you know that you need more tests.
Data Recorder
Some tools can monitor your program and collect information on the program s state in the event of a failure similar to the black box that airplanes use to diagnose crash results. You can build your own data recorder by logging significant events to a file. This functionality can be compiled in to the development version of the code and compiled out of the released version.
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