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9 CROSS-REFERENCE
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Part of the reason for these seemingly low productivity figures is that industry average numbers like these factor non-programmer time into the lines-of-codeper-day figure. Tester time, project manager time, and administrative support time are all included. Non-coding activities like requirements development and architecture work are also typically factored into those lines-of-code-per-day figures. But none of that is what takes up so much time. The single biggest activity on most projects is debugging and correcting code that doesn t work properly. Debugging and associated rework consume about 50 percent of the time on a traditional, naive software-development cycle. (See Section 3.1 for more details.) Reducing debugging by preventing errors improves productivity. Therefore, the most obvious method of shortening a development
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20. The Software-Quality Landscape
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schedule is to improve the quality of the product and decrease the amount of time spent debugging and reworking the software. This analysis is confirmed by field data. In a review of 50 development projects involving over 400 work-years of effort and almost 3 million lines of code, a study at NASA s Software Engineering Laboratory found that increased quality assurance was associated with decreased error rate but no increase or decrease in overall development cost (Card 1987). A study at IBM produced similar findings: Software projects with the lowest levels of defects had the shortest development schedules and the highest development productivity. ...Software defect removal is actually the most expensive and time-consuming form of work for software (Jones 2000). The same effect holds true on a smaller scale. In a 1985 study, 166 professional programmers wrote programs from the same specification. The resulting programs averaged 220 lines of code and a little under five hours to write. The fascinating result was that programmers who took the median time to complete their programs produced programs with the greatest number of errors. The programmers who took more or less than the median time produced programs with significantly fewer errors (DeMarco and Lister 1985). Figure 20-2 graphs the results:
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20. The Software-Quality Landscape
Page 15
1. 0 Average Defects
Over 100 500 500 Time to Complete the Program
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Figure 20-2 Neither the fastest nor the slowest development approach produces the software with the most defects.
The two slowest groups took about five times as long to achieve roughly the same defect rate as the fastest group. It s not necessarily the case that writing software without defects takes more time than writing software with defects. As the graph shows, it can take less. Admittedly, on certain kinds of projects, quality assurance costs money. If you re writing code for the space shuttle or for a medical life-support system, the degree of reliability required makes the project more expensive. People have argued for decades that fix-defects-early analysis doesn t apply to them. In the 1980s, people argued that such analysis didn t apply to them any more because structured programming was so much faster than traditional programming. In the 1990s, people argued that it didn t apply to them because object-oriented programming was so much faster than traditional techniques. In the 2000s, people assert that the argument doesn t apply to them because agile practices are so much better than traditional techniques. The pattern in these statements across the decades obvious, and, as Section 3.1 described in detail, the available data says that late corrections and late changes cost more than early corrections and changes when agile practices are used just as they did when object-oriented practices, structured practices, and machine-language practices were used. Compared to the traditional code-test-debug cycle, an enlightened softwarequality program saves money. It redistributes resources away from debugging
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20. The Software-Quality Landscape
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and into upstream quality-assurance activities. Upstream activities have more leverage on product quality than downstream activities, so the time you invest upstream saves more time downstream. The net effect is fewer defects, shorter development time, and lower costs. You ll see several more examples of the General Principle of Software Quality in the next three chapters.
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