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22. Developer Testing
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hand. When you try to do hand-calculations with an ugly number like $90,783.82, however, you re as likely to make an error in the hand-calc as you are to discover one in your program. On the other hand, a nice, even number like $20,000 makes number crunching a snap. The 0s are easy to punch into the calculator, and multiplying by 2 is something most programmers can do without using their fingers and toes. You might think that an ugly number like $90,783.82 would be more likely to reveal errors, but it s no more likely to than any other number in its equivalence class.
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This section is dedicated to the proposition that you can test best when you know as much as possible about your enemy: errors.
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Which Classes Contain the Most Errors
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It s natural to assume that defects are distributed evenly throughout your source code. If you have an average of 10 defects per 1000 lines of code, you might assume that you ll have 1 defect in a class contains 100 lines of code. This is a natural assumption, but it s wrong. Capers Jones reported a focused quality-improvement program at IBM identified 31 of 425 IMS classes as error prone. The 31 classes were repaired or completely redeveloped, and, in less than a year, customer-reported defects against IMS were reduced ten to one. Total maintenance costs were reduced by about 45%. Customer satisfaction improved from unacceptable to good (Jones 2000). Most errors tend to be concentrated in a few highly defective routines. Here is the general relationship between errors and code: Eighty percent of the errors are found in 20 percent of a project s classes or routines (Endres 1975, Gremillion 1984, Boehm 1987b, Shull et al 2002). Fifty percent of the errors are found in 5 percent of a project s classes (Jones 2000).
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9 KEY POINT
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0 HARD DATA
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These relationships might not seem so important until you recognize a few corollaries. First, 20% of a project s routines contribute 80% of the cost of development (Boehm 1987b). That doesn t necessarily mean that the 20% that cost the most
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22. Developer Testing
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are the same as the 20% with the most defects, but it s pretty doggone suggestive. Second, regardless of the exact proportion of the cost contributed by highly defective routines, highly defective routines are extremely expensive. In a classic study in the 1960s, IBM performed a study of its OS/360 operating system and found that errors were not distributed evenly across all routines but were concentrated into a few. Those error-prone routines were found to be the most expensive entities in programming (Jones 1986a). They contained as many as 50 defects per 1000 lines of code, and fixing them often cost 10 times what it took to develop the whole system. (The costs included customer support and inthe-field maintenance.)
Anot
0 HARD DATA
9 CROSS-REFERENCE
0 her class of routines that tend
to contain a lot of errors is the class of overly complex routines. For details on identifying and simplifying routines, see General Guidelines for Reducing Complexity in Section 19.6.
Third, the implication of expensive routines for development is clear. As the old expression goes, time is money. The corollary is that money is time, and if you can cut close to 80% of the cost by avoiding troublesome routines, you can cut a substantial amount of the schedule as well. This is a clear illustration of the General Principle of Software Quality, that improving quality improves the development schedule. Fourth, the implication of avoiding troublesome routines for maintenance is equally clear. Maintenance activities should be focused on identifying, redesigning, and rewriting from the ground up those routines that have been identified as error-prone. In the IMS project mentioned above, productivity of IMS releases improved about 15% after removal of the error-prone classes (Jones 2000).
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