PROMISE papers usually focus
on learning predictors for {\em non-functional requirements} such
as reliability; e.g.,  learning predictors
for defects from static code measures.
We show here that the same technology can also be used
to learn predictors and controllers for {\em functional requirements}; e.g.,
learning better settings for a software controller
for a spacecraft re-entry system.

To achieve this goal, two technologies are combined---unsupervised
clustering algorithms generated by the \AB synthesis system and the 
\TAR supervised contrast set learner.
We show that the combination of these tools
reveals more about the functional requirements than
using them in isolation.
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Layout of the paper:

1. Introduction\\
model-based SW in general;\\
parameterized models, esp. in physics-based (continuous) domains\\
This gives raise to $n^N$ testcases in a naive way\\
Cutting down these cases: many techniques (see related work).

Here, we focus on data mining and data analysis of data produced by
*many* test runs (with varying parameters - could be MC, could be
x=-1:0.1:1)

Goal is to see hidden dependencies, to find non-nominal behavior, structure
in the domain space.

2. Example/Study domain: Simulation of a spacecraft\\
2.1. The simulator\\
Simple scenario, hi-hi-level description of Trick architecture
(physics models, sensor/SC model, flight/control SW). Give some numbers of
parameters/variables.

MC testing on supercomputer

2.2. The simulation\\
As an example: re-entry. Goal: spot landing at a given target spot.
Hi-level description of some parameters that we are changing.

Fig1: a few example trajectories (altitude only or 3D(?))

3. Data Analysis:\\
What do we want to analyze and find out?\\
We are using Bayesian techniques, which can start with very simple models
(we don't want to require a complicated model to analyze our data against).

Example: Bayesian formalization of some properties

Importance of classification in high dimensions.

Here two approaches:\\
(unsupervised) clustering with AutoBayes (mixture
of Gaussians)

binary classification and treatment learning

We will illustrate both machine learning techniques and tools with out
selected dataset.

3.1. Mixture Models

Description of model and AB specification 
(reuse from other papers)

Short Description of AutoBayes

Fig: system architecture

Clustering results\\
Description of input data

Fig: Clustering of targets (2D or 3D) - long-lat with obfuscated
target spot (or just do a coord transformation toward Kennedy :-)

Fig: log-likelihood over n-classes

Fig: correlation of class membership with variied parameters
(incidentally longitude and azimuth)

Discussion of figure

3.2 Treatment Learning

Tim's background treatment, but without the golf example.

Results of experiment

Table with most effective treatments

Figure: target distribution before-after treatment (does this make sense?)

Discussion of results

NB: Does it make sense to use the AutoBayes classifier instead of the
\BORE classifier? In the most simple case, it would replace the good/bad
threshold (25/75 ???) by a dynamic measure (basically the class probabilities
(``best class'' vs $\sum$ other classes)


4. Discussion

Relate the data mining/clustering results back to SW engineering.
What do the results mean w.r.t. coverage, V\&V, etc.


5. Conclusions
}
