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27. How Program Size Affects Construction
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Source: Derived from data in A Survey of Software Engineering Practice: Tools, Methods, and Results (Beck and Perkins 1983), Agile Software Development Ecosystems (Highsmith 2002), and Balancing Agility and Discipline (Boehm and Turner 2003).
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27.3 Effect of Project Size on Errors
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Both the quantity and the kinds of errors are affected by project size. You might not think that the kinds of errors would be affected, but as project size increases, a larger percentage of errors can usually be attributed to mistakes in requirements and design. Here s an illustration:
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for more details on errors, see 1 Section 22.4, Typical 2 Errors.
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0 CROSS-REFERENCE
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Construction Errors from Each Activity Design
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On some projects, this percentage of errors may also be from construction.
0% 2K
Requirements 8K 32K 128K Project Size in Lines of Code 512K
F27xx02
Figure 27-2 As project size increases, errors usually come more from requirements and design. Sometimes they still come primarily from construction. Sources: Software Engineering Economics (Boehm 1981), Measuring and Managing Software Maintenance (Grady 1987), and Estimating Software Costs (Jones 1998).
On small projects, construction errors make up about 75 percent of all the errors found. Methodology has less influence on code quality, and the biggest influence on program quality is often the skill of the individual writing the program (Jones 1998). On larger projects, construction errors can taper off to about 50 percent of the total errors; requirements and architecture errors make up the difference. Presumably this is related to the fact that more requirements development and
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27. How Program Size Affects Construction
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architectural design are required on large projects, so the opportunity for errors arising out of those activities is proportionally larger. In some very large projects, however, the proportion of construction errors remains high; sometimes even with 500,000 lines of code, up to 75 percent of the errors can be attributed to construction (Grady 1987). As the kinds of defects change with size, so do the numbers of defects. You would naturally expect a project that s twice as large as another to have twice as many errors. But the density of defects, the number of defects per line of code, increases. The product that s twice as large is likely to have more than twice as many errors. Table 27-1 shows the range of defect densities you can expect on projects of various sizes:
Table 27-1. Project Size and Error Density
The data in this table represents average performance. A handful of organizations have reported better error rates than the minimums shown here. For examples, see How Many Errors Should You Expect to Find in Section 22.4.
CROSS-REFERENCE
4 KEY POINT
Project Size (in Lines of Code) Smaller than 2K 2K-16K 16K-64K 64K-512K 512K or more
Error Density 0-25 errors per thousand lines of code (KLOC) 0-40 errors per KLOC 0.5-50 errors per KLOC 2-70 errors per KLOC 4-100 errors per KLOC
Source: Program Quality and Programmer Productivity (Jones 1977), Estimating Software Costs (Jones 1998).
The data in this table was derived from specific projects, and the numbers may bear little resemblance to those for the projects you ve worked on. As a snapshot of the industry, however, the data is illuminating. It indicates that the number of errors increases dramatically as project size increases, with very large projects having up to four times as many errors per line of code as small projects. The data also implies that up to a certain size, it s possible to write error-free code; above that size, errors creep in regardless of the measures you take to prevent them.
27.4 Effect of Project Size on Productivity
Productivity has a lot in common with software quality when it comes to project size. At small sizes (2000 lines of code or smaller), the single biggest influence on productivity is the skill of the individual programmer (Jones 1998). As project size increases, team size and organization become greater influences on productivity.
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7 HARD DATA
How big does a project need to be before team size begins to affect productivity in Prototyping Versus Specifying: a Multiproject Experiment, Boehm, Gray, and Seewaldt reported that smaller teams completed their projects with 39 percent higher productivity than larger teams. The size of the teams Two people for the small projects, three for the large (1984). Table 27-2 gives the inside scoop on the general relationship between project size and productivity.
Table 27-2. Project Size and Productivity Project Size (in Lines of Code) 1K 10K 100K 1,000K 10,000K Lines of Code per Staff-Year (Cocomo II nominal in parentheses) 2,500 25,000 (4,000) 2,000 25,000 (3,200) 1,000 20,000 (2,600) 700 10,000 (2,000) 300 5,000 (1,600)
Source: Derived from data in Measures for Excellence (Putnam and Meyers 1992), Industrial Strength Software (Putnam and Meyers 1997), Software Cost Estimation with Cocomo II (Boehm et al, 2000), and Software Development Worldwide: The State of the Practice (Cusumano et al 2003).
Productivity is substantially determined by the kind of software you re working on, personnel quality, programming language, methodology, product complexity, programming environment, tool support, how lines of code are counted, how non-programmer support effort is factored into the lines of code per staff-year figure, and many other factors, so the specific figures in Table 27-2 vary dramatically. Realize, however, that the general trend the numbers show is significant. Productivity on small projects can be 2-3 times as high as productivity on large projects, and productivity can vary by a factor of 5-10 from the smallest projects to the largest.
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