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Several researchers have tried to classify errors by type and determine the extent to which each kind of error occurs. Every programmer has a list of errors that have been particularly troublesome: off-by-one errors, forgetting to reinitialize a loop variable, and so on. The checklists presented throughout the book provide more details. Boris Beizer combined data from several studies, arriving at an exceptionally detailed error taxonomy (Beizer 1990). Following is a summary of his results:
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25.18% 22.44% 16.19% 9.88% 8.98% Structural Data Functionality as implemented Construction Integration
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For a list of all the checklists in 3 the book, see the list of 4 checklists following the table 5 of contents.
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2 CROSS-REFERENCE
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de Complete
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22. Developer Testing
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Functional requirements Test definition or execution System, software architecture
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Unspecified Beizer reported his results to a precise two decimal places, but the research into error types has generally been inconclusive. Different studies report wildly different kinds of errors, and studies that report on similar kinds of errors arrive at wildly different results, results that differ by 50% rather than by hundredths of a percentage point.
Given the wide variations in reports, combining results from multiple studies as Beizer has done probably doesn t produce meaningful data. But even if the data isn t conclusive, some of it is suggestive. Here are some of the suggestions that can be derived from it:
8 HARD DATA
The scope of most errors is fairly limited One study found that 85% of errors could be corrected without modifying more than one routine (Endres 1975). Many errors are outside the domain of construction Researchers conducting a series of 97 interviews found that the three most common sources of errors were thin application-domain knowledge, fluctuating and conflicting requirements, and communication and coordination breakdown (Curtis, Krasner, and Iscoe 1988).
If you see hoof prints, think horses not zebras. The OS is probably not broken. And the database is probably just fine. Andy Hunt and Dave Thomas
Most construction errors are the programmers fault A pair of studies performed many years ago found that, of total errors reported, roughly 95% are caused by programmers, 2% by systems software (the compiler and the operating system), 2% by some other software, and 1% by the hardware (Brown and Sampson 1973, Ostrand and Weyuker 1984). Systems software and development tools are used by many more people today than they were in the 1970s and 1980s, and so my best guess is that, today, an even higher percentage of errors are the programmer s fault. Clerical errors (typos) are a surprisingly common source of problems One study found that 36% of all construction errors were clerical mistakes (Weiss 1975). A 1987 study of almost 3 million lines of flight-dynamics software found that 18% of all errors were clerical (Card 1987). Another study found that 4% of all errors were spelling errors in messages (Endres 1975). In one of my programs, a colleague found several spelling errors simply by running all the strings from the executable file through a spelling checker. Attention to detail counts. If you doubt that, consider that three of the most expensive software errors of all time cost $1.6 billion, $900 million, and $245 million.
4 HARD DATA
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Each one involved the change of a single character in a previously correct program (Weinberg 1983).
Misunderstanding the design is a recurring theme in studies of programmer errors Beizer s compilation study, for what it s worth, found that 16.19% of the errors grew out of misinterpretations of the design (Beizer 1990). Another study found that 19% of the errors resulted from misunderstood design (Weiss 1975). It s worthwhile to take the time you need to understand the design thoroughly. Such time doesn t produce immediate dividends (you don t necessarily look like you re working), but it pays off over the life of the project. Most errors are easy to fix About 85% of errors can be fixed in less than a few hours. About 15% can be fixed in a few hours to a few days. And about 1% take longer (Weiss 1975, Ostrand and Weyuker 1984). This result is supported by Barry Boehm s observation that about 20% of the errors take about 80% of the resources to fix (Boehm 1987b). Avoid as many of the hard errors as you can by doing requirements and design reviews upstream. Handle the numerous small errors as efficiently as you can. It s a good idea to measure your own organization s experiences with errors The diversity of results cited in this section indicates that people in different organizations have tremendously different experiences. That makes it hard to apply other organizations experiences to yours. Some results go against common intuition; you might need to supplement your intuition with other tools. A good first step is to start measuring your process so that you know where the problems are.
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