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any combination of two methods (including having two independent groups using the same method) increased the total number of defects found by a factor of almost 2. A study at NASA s Software Engineering Laboratory also reported that different people tend to find different defects. Only 29 percent of the errors found by code reading were found by both of two code readers (Kouchakdjian, Green, and Basili 1989). Glenford Myers points out that human processes (inspections and walkthroughs, for instance) tend to be better than computer-based testing at finding certain kinds of errors and that the opposite is true for other kinds of errors (1979). This result was confirmed in a later study, which found that code reading detected more interface defects and functional testing detected more control defects (Basili, Selby, and Hutchens 1986). Test guru Boris Beizer reports that informal test approaches typically achieve only 50-60% test coverage unless you re using a coverage analyzer (Johnson 1994). The upshot is that defect-detection methods work better in combination than they do singly. Jones made the same point when he observed that cumulative defectdetection efficiency is significantly higher than that of any individual technique. The outlook for the effectiveness of testing used by itself is bleak. Jones points out that a combination of unit testing, functional testing, and system testing often results in a cumulative defect detection of less than 60 percent, which is usually inadequate for production software. This data can also be used to understand why programmers who begin working with a disciplined defect removal technique such as Extreme Programming experience higher defect removal levels than they have experienced previously. As Table 20-2 illustrates, the set of defect removal practices used in Extreme Programming would be expected to achieve about 90% defect removal efficiency in the average case and 97% in the best case, which is far better than the industry average of 85% defect removal. This result is not due to any mysterious synergy among extreme programming s practices; it is a predictable outcome of using these specific defect removal practices. Other combinations of practices can work equally well or better, and the determination of which specific defect removal practices will be used to achieve the desired quality level is one part of effective project planning.
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Table 20-2. Extreme Programming s Estimated Defect-Detection Rate Removal Step Informal design reviews (pair programming) Informal code reviews (pair programming) Lowest Rate 25% 20% Modal Rate 35% 25% Highest Rate 40% 35%
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Removal Step Personal desk-checking of code Unit test Integration test Regression test Expected cumulative defect removal efficiency
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Lowest Rate 20% 15% 25% 15% ~74%
Modal Rate 40% 30% 35% 25% ~90%
Highest Rate 60% 50% 40% 30% ~97%
Cost of Finding Defects
Some defect-detection practices cost more than others. The most economical practices result in the least cost per defect found, all other things being equal. The qualification that all other things must be equal is important because per defect cost is influenced by the total number of defects found, the stage at which each defect is found, and other factors besides the economics of a specific defect-detection technique. In the 1978 Myers study cited earlier, the difference in cost per defect between the two execution-testing methods (with and without source code) wasn t statistically significant, but the walkthrough/inspection method cost over twice as much per defect found as the test methods (Myers 1978). These results have been consistent for decades. A later study at IBM found that only 3.5 staff hours were needed to find each error using code inspections, whereas 15-25 hours were needed to find each error through testing (Kaplan 1995). Organizations tend to become more effective at doing inspections as they gain experience. Consequently, more recent studies have shown conclusively that inspections are cheaper than testing. One study of three releases of a system showed that on the first release, inspections found only 15 percent of the errors found with all techniques. On the second release, inspections found 41 percent, and on the third, 61 percent (Humphrey 1989). If this history were applied to Myers s study, it might turn out that inspections would eventually cost half as much per defect as testing instead of twice as much. A study at the Software Engineering Laboratory found that code reading detected about 80 percent more faults per hour than testing (Basili and Selby 1987).
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