motivation

- quickly building, and rebuilding coverage models
    - the google experience. large teams of agile programmers always 
      changing the code base
    - for them, traditional testing models fail. 
        - no ``test'' environment
        - they have to ``test'' in the production environment 
          (no such thign as a copy of the web)

- optimize GAs
    - a standard GAs mutate binary strings  of size N has 2^N options
    - reducing N is a paramount
    - here, our genes are much more complex. (jamie, what can we say here?)
      but the principle remains.

- testing the keys effect menzies & singh speculate that most models
    - have a small number of critical key variables that set
      everything else
  
    - keys effect has been reported many times, with many different
      names, over the last 40 years

    - in several applications, they have compared the performance of
      the whole to just that of the part.
  
        - and the keys effect suggest that the parts will do just as well
          as the whole

    - here, yet again, we report the keys effect. coverage models
      built for the part do just as well as the whole


pre-experimental concerns: 

- no common ordering to the chromosone type rankings
   - not true. same effect in all results.
       - all types ranked the same way
       - in all data sets, we losts a lot after removing top 6

- fss would cripple coverage models
   - not so, usuall the same, sometimes much better (EnumType)

- fss would have no effect
   - this is a good thing. if the same processing can be achieved 
     with less, then so be it

future work

- shown here that can remove most of the chromosone types and 
  still build goog coverage models

- now, we need to show how _early_ we can recognize those 
   chromosome types

- also, we need to change the GA to randomize irrlevant types 
  and only perform breeding using most important types

 
