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Team Ranking on Each Objective
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Objective Team Was Told to Optimize Minimum memory use Most readable output Most readable code Least code Minimum programming time
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Minimum memory Output readability Program readability Minimum statements
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20. The Software-Quality Landscape
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Team Ranking on Each Objective
Objective Team Was Told to Optimize Minimum memory use Most readable output Most readable code Least code Minimum programming time
Minimum programming time
Source: Adapted from Goals and Performance in Computer Programming (Weinberg and Schulman 1974).
8 HARD DATA
The results of this study were remarkable. Four of the five teams finished first in the objective they were told to optimize. The other team finished second in its objective. None of the teams did consistently well in all objectives. The surprising implication is that people actually do what you ask them to do. Programmers have high achievement motivation: They will work to the objectives specified, but they must be told what the objectives are. The second implication is that, as expected, objectives conflict and it s generally not possible to do well on all of them.
20.3 Relative Effectiveness of Quality Techniques
The various quality-assurance practices don t all have the same effectiveness. Many techniques have been studied, and their effectiveness at detecting and removing defects is known. This and several other aspects of the effectiveness of the quality-assurance practices are discussed in this section.
Percentage of Defects Detected
If builders built buildings the way programmers wrote programs, then the first woodpecker that came along would destroy civilization. Gerald Weinberg
Some practices are better at detecting defects than others, and different methods find different kinds of defects. One way to evaluate defect-detection methods is to determine the percentage of defects they find out of the total defects found over the life of a product. Table 20-1 shows the percentages of defects detected by several common defect-detection techniques.
Table 20-1. Defect-Detection Rates Removal Step Informal design reviews Formal design inspections Informal code reviews Lowest Rate 25% 45% 20% Modal Rate 35% 55% 25% Highest Rate 40% 65% 35%
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20. The Software-Quality Landscape
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Removal Step Formal code inspections Modeling or prototyping Personal desk-checking of code Unit test New function (component) test Integration test Regression test System test Low-volume beta test (<10 sites) High-volume beta test (>1,000 sites)
Lowest Rate 45% 35% 20% 15% 20% 25% 15% 25% 25% 60%
Modal Rate 60% 65% 40% 30% 30% 35% 25% 40% 35% 75%
Highest Rate 70% 80% 60% 50% 35% 40% 30% 55% 40% 85%
Source: Adapted from Programming Productivity (Jones 1986a), Software DefectRemoval Efficiency (Jones 1996), and What We Have Learned About Fighting Defects (Shull et al 2002).
2 HARD DATA
The most interesting fact that this data reveals is that the modal rates don t rise above 75 percent for any single technique, and the techniques average about 40 percent. Moreover, for the most common kind of defect detection, unit testing, the modal rate is only 30 percent. The typical organization uses a test-heavy defect-removal approach, and achieves only about 85% defect removal efficiency. Leading organizations use a wider variety of techniques and achieve defect removal efficiencies of 95 percent or higher (Jones 2000). The strong implication is that if project developers are striving for a higher defect-detection rate, they need to use a combination of techniques. A classic study by Glenford Myers confirmed this implication (Myers 1978b). Myers studied a group of programmers with a minimum of 7 and an average of 11 years of professional experience. Using a program with 15 known errors, he had each programmer look for errors using one of these techniques: Execution testing against the specification Execution testing against the specification with the source code Walkthrough/inspection using the specification and the source code
8 HARD DATA
Myers found a huge variation in the number of defects detected in the program, ranging from 1.0 to 9.0 defects found. The average number found was 5.1, or about a third of those known. When used individually, no method had a statistically significant advantage over any of the others. The variety of errors people found was so great, however, that
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