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How do Programmers Spend Their Time
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Programmers spend their time programming, but they also spend time in meetings, on training, on reading their mail, and on just thinking. A 1964 study at Bell Laboratories found that programmers spent their time this way:
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Table 28-3. One View of How Programmers Spend Their Time
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Personal
Meetings
Training
Technical Manuals
Talk or listen Talk with manager Telephone Read Write/record Away or out Walking Miscellaneous Totals
17% 1% 2%
1% 2% 1% 2%
14% 13% 4% 2% 2% 35% 2% 3% 29% 1% 1% 3% 13% 7% 6% 4% 6%
1% 1% 5% 2%
Source: Research Studies of Programmers and Programming (Bairdain 1964, reported in Boehm 1981).
This data is based on a time-and-motion study of 70 programmers. The data is old, and the proportions of time spent in the different activities would vary among programmers, but the results are nonetheless illuminating. About 30 percent of a programmer s time is spent in non-technical activities that don t directly help the project: walking, personal business, and so on. Programmers in this study spent 6 percent of their time walking; that s about 2.5 hours a week, about 125 hours a year. That might not seem like much until you realize that programmers spend as much time each year walking as they spend in training, three times as much time as they spend reading technical manuals, and six times as much as they spend talking with their managers. I personally have not seen much change in this pattern today.
Variation in Performance and Quality
Talent and effort among individual programmers vary tremendously, as they do in all fields. One study found that in a variety of professions writing, football, invention, police work, and aircraft piloting the top 20 percent of the people produced about 50 percent of the output (Augustine 1979). The results of the study are based on an analysis of productivity data such as touchdowns, patents, solved cases, and so on. Since some people make no tangible contribution whatsoever (quarterbacks who make no touchdowns, inventors who own no patents, detectives who don t close cases, and so on), the data probably understates the actual variation in productivity.
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Page 24
In programming specifically, many studies have shown order-of-magnitude differences in the quality of the programs written, the sizes of the programs written, and the productivity of programmers.
Individual Variation
The original study that showed huge variations in individual programming productivity was conducted in the late 1960s by Sackman, Erikson, and Grant (1968). They studied professional programmers with an average of 7 years experience and found that the ratio of initial coding time between the best and worst programmers was about 20 to 1; the ratio of debugging times over 25 to 1; of program size 5 to 1; and of program execution speed about 10 to 1. They found no relationship between a programmer s amount of experience and code quality or productivity. Although specific ratios such as 25 to 1 aren t particularly meaningful, more general statements such as There are order-of-magnitude differences among programmers are meaningful and have been confirmed by many other studies of professional programmers (Curtis 1981, Mills 1983, DeMarco and Lister 1985, Curtis et al. 1986, Card 1987, Boehm and Papaccio 1988, Valett and McGarry 1989, Boehm et al 2000).
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Team Variation
Programming teams also exhibit sizable differences in software quality and productivity. Good programmers tend to cluster, as do bad programmers, an observation that has been confirmed by a study of 166 professional programmers from 18 organizations (Demarco and Lister 1999). In one study of seven identical projects, the efforts expended varied by a factor of 3.4 to 1 and program sizes by a factor of 3 to 1 (Boehm, Gray, and Seewaldt 1984). In spite of the productivity range, the programmers in this study were not a diverse group. They were all professional programmers with several years of experience who were enrolled in a computer-science graduate program. It s reasonable to assume that a study of a less homogeneous group would turn up even greater differences. An earlier study of programming teams observed a 5-to-1 difference in program size and a 2.6-to-1 variation in the time required for a team to complete the same project (Weinberg and Schulman 1974). After reviewing data more than 20 years of data in constructing the Cocomo II estimation model, Barry Boehm and other researchers concluded that developing a program with a team in the 15th percentile of programmers ranked by ability typically requires about 3.5 times as many work-months as developing a program with a team in the 90th percentile (Boehm et al 2000). Boehm and other
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