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else s fault and then having to recant publicly later when you find out that it was your defect after all.

Debugging consists of finding the defect and fixing it. Finding the defect (and understanding it) is usually 90 percent of the work. Fortunately, you don t have to make a pact with Satan in order to find an approach to debugging that s better than random guessing. Contrary to what the Devil wants you to believe, debugging by thinking about the problem is much more effective and interesting than debugging with an eye of newt and the dust of a frog s ear. Suppose you were asked to solve a murder mystery. Which would be more interesting: going door to door throughout the county, checking every person s alibi for the night of October 17, or finding a few clues and deducing the murderer s identity Most people would rather deduce the person s identity, and most programmers find the intellectual approach to debugging more satisfying. Even better, the effective programmers who debug in one-twentieth the time of the ineffective programmers aren t randomly guessing about how to fix the program. They re using the scientific method.

Here are the steps you go through when you use the scientific method: 1. Gather data through repeatable experiments. 2. Form a hypothesis that accounts for the relevant data. 3. Design an experiment to prove or disprove the hypothesis. 4. Prove or disprove the hypothesis. 5. Repeat as needed. This process has many parallels in debugging. Here s an effective approach for finding a defect: 1. Stabilize the error. 2. Locate the source of the error (the fault ).

a. Gather the data that produces the defect. b. Analyze the data that has been gathered and form a hypothesis about the defect. c. Determine how to prove or disprove the hypothesis, either by testing the program or by examining the code. d. Prove or disprove the hypothesis using the procedure identified in 2(c). 3. Fix the defect. 4. Test the fix. 5. Look for similar errors. The first step is similar to the scientific method s first step in that it relies on repeatability. The defect is easier to diagnose if you can make it occur reliably. The second step uses the first four steps of the scientific method. You gather the test data that divulged the defect, analyze the data that has been produced, and form a hypothesis about the source of the error. You design a test case or an inspection to evaluate the hypothesis and then declare success or renew your efforts, as appropriate. Let s look at each of the steps in conjunction with an example. Assume that you have an employee database program that has an intermittent error. The program is supposed to print a list of employees and their income-tax withholdings in alphabetical order. Here s part of the output:

Formatting, Fred Freeform Goto, Gary Modula, Mildred Many-Loop, Mavis Statement, Sue Switch Whileloop, Wendy $5,877 $1,666 $10,788 $8,889 $4,000 $7,860

If a defect doesn t occur reliably, it s almost impossible to diagnose. Making an intermittent defect occur predictably is one of the most challenging tasks in debugging.

An error that doesn t occur predictably is usually an initialization error or a dangling-pointer problem. If the calculation of a sum is right sometimes and wrong sometimes, a variable involved in the calculation probably isn t being initialized properly most of the time it just happens to start at 0. If the problem is a strange and unpredictable phenomenon and you re using pointers, you almost certainly have an uninitialized pointer or are using a pointer after the memory that it points to has been deallocated. Stabilizing an error usually requires more than finding a test case that produces the error. It includes narrowing the test case to the simplest one that still produces the error. If you work in an organization that has an independent test team, sometimes it s the team s job to make the test cases simple. Most of the time, it s your job. To simplify the test case, you bring the scientific method into play again. Suppose you have 10 factors that, used in combination, produce the error. Form a hypothesis about which factors were irrelevant to producing the error. Change the supposedly irrelevant factors, and rerun the test case. If you still get the error, you can eliminate those factors and you ve simplified the test. Then you can try to simplify the test further. If you don t get the error, you ve disproved that specific hypothesis, and you know more than you did before. It might be that some subtly different change would still produce the error, but you know at least one specific change that does not. In the employee withholdings example, when the program is run initially, ManyLoop, Mavis is listed after Modula, Mildred. When the program is run a second time, however, the list is fine:

Formatting, Fred Freeform Goto, Gary Many-Loop, Mavis Modula, Mildred Statement, Sue Switch Whileloop, Wendy $5,877 $1,666 $8,889 $10,788 $4,000 $7,860

It isn t until Fruit-Loop, Frita is entered and shows up in an incorrect position that you remember that Modula, Mildred had been entered just before she showed up in the wrong spot too. What s odd about both cases is that they were entered singly. Usually, employees are entered in groups. You hypothesize: The problem has something to do with entering a single new employee. If this is true, running the program again should put Fruit-Loop, Frita in the right position. Here s the result of a second run: