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If you do enter your project data into the ISBSG Repository, you have the best of both worlds You can extract your projects to derive the project delivery rates you have achieved, and you can extract similar projects from other organizations for comparison
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Use of the ISBSG Data
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Industry data such as the ISBSG data is useful in estimating, in particular when you have no relevant experience data from your own organization to draw upon, or when you are planning a project that has characteristics unlike any of your previous projects At the very least, industry data will give you some indication of the reasonableness of your own figures
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The ISBSG Repository
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The data in the ISBSG Repository is not random project data, but is more likely to be representative of projects with higher than average productivity The key reason for higher than average productivity being represented lies in the method of ISBSG Repository collection Many corporate repositories have been compiled by consultancy companies that were invited into organizations to benchmark them against the industry This is often the result of serious concerns about the efficiency of the IT division As such, these corporate repositories tend to reflect a random sample of industry projects The source of the ISBSG Repository has been different in that the project data has been submitted voluntarily, by software practitioners who have a genuine interest in maximizing their productivity These practitioners could reasonably be regarded as mature in their measurement philosophy and practices, and have proactively sought to benchmark their projects against the world Other significant differentiating attributes of the ISBSG data include: 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 measurement2 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
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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
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Using project history Databases
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Organizations also choose which of their projects they submit They might choose typical projects, but they might choose only their best projects The majority of the projects in the repository are less than 500 function points in size There are few really big projects In determining which data to collect, the ISBSG philosophy has been that a minimum set of data will be collected that is meaningful, readily available, and objective As such, much of the soft (subjective) data, which is important to estimating, is not collected ISBSG does collect some data on people factors and product and process quality, but not currently in areas like the general novelty of the project (has this type of system been built before ), the complexity of the problem being addressed, the stability of user requirements, and project constraints 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 selfcontained, internally consistent, and contains no apparent anomalies The repository is therefore a very valuable collection of data for estimation
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Guidelines for Use of the ISBSG Data
As the ISBSG project history data is readily available to IT practitioners and researchers, it is important that its users have a sound knowledge of the data are aware of its strengths, limitations, and positioning prior to analyzing or using it 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, and not just treat it as numbers to be used without selectivity What project types can be legitimately compared or analyzed together Here are some examples: Project Rating The ISBSG considers that projects with a data quality rating of A or B are suitable for statistical analysis Projects rated C or D may still provide valuable data, but uncertainty about some of their size or effort values means that it is best not to include them in your estimation data sets Normalized Effort3 For effort, consider what risk and gain is involved in using normalized effort The Summary Work
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