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If you intend using the ISBSG data for benchmarking or to help you with software estimation, then you need to be aware of the likely maturity level of the companies that submitted the project data that you are benchmarking against If you are a researcher who is going to use the ISBSG data for work that will report on the performance of the IT industry, you need to consider whether the ISBSG data (and particularly a selected subset of it) is representative of the industry
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Appendix A:
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What is in the iSBSG Repository
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ISBSG Project Data Positioning
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The ISBSG project data is not necessarily representative of the software industry The ISBSG believes that the projects in the repository are representative of the more productive projects in the industry, rather than of industry norms There are a number of reasons for this Some organizations simply cannot contribute to the repository The criteria for including a project in the repository generally exclude organizations that do not use functional size measurement (FSM)1 They also exclude projects for which work effort (in person-hours) is not available Only organizations that collect the necessary metrics can contribute to the repository Organizations with software metrics programs are likely to be among the more mature software development organizations Organizations also choose which of their projects they submit They might choose typical projects, but they might choose only their best projects It is also worth noting that the majority of the projects in the repository are less than 2,000 function points in size Very few are really big projects These considerations do not lessen the value of the data in the repository The focus of the repository is as much on understanding best practice in the IT industry as on overall averages However, the key metrics have been studied and tested The results of this work demonstrate that the sample represented by the repository is self-contained, internally consistent, and contains no apparent anomalies The repository is therefore a very valuable collection of data for a number of avenues of analysis, benchmarking, and estimation
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When performing statistical analyses, it is very important to make sure that like is compared with like, that apples are not compared with oranges For this reason, it should be rare to find the entire repository analyzed as a single sample Subsets of projects should be analyzed, so that it makes sense to compare projects within a subset
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Selecting a Suitable Data Subset
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It is important to give careful thought to the project data that you will include in any data set that you plan to use You need to think about the meaning of the data, not just to treat it as numbers to be used without selectivity What project types can be legitimately compared or analyzed together
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Although the repository does accept projects that have been sized using methods other than functional units (for example, LOC and use case points), the ISBSG does not perform validation on these size measures; it simply records them for general information
practical Software project estimation
Here are some examples: Functional sizing methods You shouldn t mix pre-IFPUG V4 projects with V4 and post-V4 (the sizing changed with that release) New development projects sized using the NESMA standard can be included with IFPUG V4+ projects Use the Count Approach field in the data (and perhaps also the FP Standards and Reference Table Approach fields), to select projects that use the same sizing method that you use Normalized Effort2 For effort, give consideration to what risk and gain is involved in using normalized effort The Summary Work Effort shown in the data is the total effort for the project What is counted within that total varies, because different projects record effort at different levels of detail (see the Resource Level and Recording Method fields) A resource level of 1 means that only the effort of the development team is recorded; 2 means that support team effort is also recorded; 3 adds computer operations; and 4 adds effort expended by the end user or client Two things you might do to make sure you compare effort appropriately: Select only those projects that record the same effort detail as you do You don t have to ignore every other project you can use rules of thumb to translate approximately between different levels of effort Previous analysis of projects in the ISBSG Repository shows that Level 2 effort is about 10 12 percent more than Level 1, Level 3 adds about another 1 or 2 percent, and Level 4 is about 20 25 percent more than Level 1
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