\subsection{Search-based Software Engineering}
Previously~\cite{me09i}, we have explored the connection of SEESAW to search-based SE (SBSE)~\cite{harman04}.
In summary, 
SBSE uses optimization techniques
from
operations research and meta-heuristic search (e.g., simulated annealing and genetic algorithms)
to hunt for near-optimal
solutions to complex and over-constrained
software engineering problems. 
SBSE has been applied to many
problems in software engineering (e.g., requirements engineering~\cite{jalali08})
 but most often in the field of software testing~\cite{andrews07}.
Harman's writing inspired us to try simulated annealing (SA) to search the what-ifs in untuned COCOMO
models~\cite{me07f}. 
For quality optimization, however, we found that search methods
taken from the constraint satisfaction literature
out-perform SA~\cite{me09i}.

\subsection{Model-lite}
We said above that CBR was {\em model-lite}, but not {\em model-free}.
We hesitate to call CBR {\em model-free}, lest we incur the wrath
of Janet Kolodner or Roger Shank~\cite{shank77}.
Kolodner and Shank regard CBR as a {\em model} of human cognition
where knowledge in a context-dependent
manner, according to the task at hand. This construct may differ from context to context
but the search mechanisms by which the construct is built (CBR) is constant.

To expand on that point, we note that ``model'' has at least two
definitions:
\be
\item A hypothetical description of a complex entity or process. 
\item A plan to create, according to a model or models. 
\ee 
The first definition is closest to Shepperd's definition of ``model-based systems''. According to Shepperd~\cite{shepperd07} software effort estimation methods separate into ``human-centric'' techniques and ``model-based'' techniques. In the former, humans produce their recommendations without using some externalizable representation. In the latter, a variety of techniques may be used which, according to Shepperd, divide into algorithmic/parametric models (like COCOMO) and induced prediction systems (which include regression, rule induction, CBR, and many others). 

We can marry Shepperd's view with that of Kolodner and Shank by specializing the definition
of model-based systems. Extending Shepperd's ontology, we say that model-based systems can be sorted according to how much modeling they assume prior to induction. At one end of that sort order, we have parametric models like COCOMO. We call these {\em model-heavy} since they conform to the first definition of ``model'', shown above. At the other end of that sort are  the {\em model-lite} methods like CBR. These model-lite methods conform to the second definition of ``model''. Note that this second definition is closest to Kolodner and Shank's view on CBR; i.e. the CBR model is a recipe for generating context-dependent knowledge.
