Database Validation Best Practices

Database validation is an important step in ensuring that you have quality data in your historical database.  I've talked before about the importance of collecting project data and what you can do with your own data, but it all hinges on having thoroughly vetted project history.

Although it's nice to have every tab in SLIM-DataManager filled out, we really only need three key pieces of information to calculate PI:

  • Size (Function Unit): if the function unit is not SLOC, a gearing factor should be provided (97.3% of projects in the database report total size)
  • Phase 3 duration or start and end dates (99.9% of projects in the database report phase 3 duration)
  • Phase 3 effort (99.9% of projects in the database report phase 3 effort)

These fields can be thought of as the desired minimum information needed, but even if one is missing, you may not want to delete the project from the database. A project that is missing effort data, for instance, will not have a PI but could be used to query a subset of projects for average duration by size. Likewise, a project with no size will not have a PI, but does contain effort and duration information that could be useful for calculating the average time to market for a division. However, if possible, it is a good idea to fill out at least these three fields.

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SLIM-Metrics Data SLIM-DataManager Database

Why Are Conversion Projects Less Productive than Development?

While doing research on projects counted in function points, the sample size was large enough (over 2000 projects) to allow me to compare the productivity of different project types.  The QSM database uses these project categories:

  • New Development (> 75% new functionality)
  • Major Enhancement (25% - 75% new functionality)
  • Minor Enhancement (5% - 25% new functionality)
  • Conversion (< 5% new functionality)
  • Maintenance

I calculated the normalized PI’s for projects in each development classification compared to the QSM Business trend lines.  The advantage of this is that it takes into consideration the impact of size and shows how the productivity of each project “application type” differs from the QSM Business IT average.  The datasets included medium and high confidence IT projects completed since 2000.  When I obtained the results, I went back over my selection process and calculations to make sure I hadn’t made a mistake.  The numbers were that surprising.  But, no, I hadn’t fat fingered anything (neither physically nor mentally).  Average productivity for conversion projects  was more than a standard deviation below the QSM Business IT average.

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SLIM-Estimate Function Points Database