This is the help for STAR. 

XOMO [1] is the initial tool which inspired this one. These two tools
both aim at minimizing Effort, Defects, and Threat based on the COCOMO
II [2], Coqualmo, and Threat Models [3] for given project parameters
that are based on these models. However, the two tools take two 
differet approaches for tackling the same problem. While XOMO uses 
Tar3 treatment learning [4], STAR uses simulated annealing [5], 
combined with best or rest discretization (bore) and a linear back 
select algorithm to show the best treatments that can be chosen (i.e. 
the ones discarded last in the back select process). The temperature 
of the SA algorithm is set to exp(A*k/kmax), where kmax is the maximum 
number if iterations allowed, k is the number of iterations so far, 
and A is a constant that is chosen.

The following are options allowed with this version of STAR:

	Option		Description
	------		-----------
	-n or -N	Bore for worst: This allows the user to get the
			worst treatments instead of the best.

	-h or -H	To display this help file and exit.

	-l or -L	To enable logging and post processing: Logging
			as in keeping a log of the results of the
			simulated annealer, and post processing being
			the back select and simulations.

	-s or -S	To enable strategic analysis

	-t or -T	To enable tactical analysis
	
	-er or -ER	To enable energy ranking

	-f or -F 	Used in the form of "-f [project name] [file
			path]", this indicates the project being
			analyzed and the location of the project files.
			This option must be included for STAR to
			work. Also, both the *.values and *.ranges for
			the project files must be in the same folder
			specified by [file path].

	-b or -B	Used in the form of "-b [value]". Sets the
			ratio that bore will use for discretization.

	-e or -E	Used in the form of "-e [value]". Sets the
			min energy threshhold for simulated annealing.

	-k or -K	Used in the form of "-k [value]". Sets the
			max number of iterations for simulated annealing.

There are several files included with STAR. The following is a brief
description of each: 

	File Name		Description
	---------		-----------
	graph			This is a bash script that uses
				generated simulation data to produce
				plots in the command line shell.

	graphPNG		This is a bash script that uses
				generated simulation data to produce
				plots in *.png format.

	/eg			This folder contains sample bash
				scripts of running STAR.

	/STAR_projects		This folder contains project files.
				Note that for a single project, the
				ranges and values files are in the form
				"[Project Name].ranges" and "[Project
				Name].values".

Inorder to get started simply make. All the needed files will be in 
$HOME/STAR/, while the executable will be in $HOME/bin. The following is 
an example:

	STAR -l -f OSP STAR_projects/

The graph script can be executed as follows:

	./graph

After running this script, plots will be dumped onto the command line. 
Note: gnuplot is needed for this.

References:
-----------
[1] T. Menzies. RST milestone 1.1.5.9: Applying trade space analysis to
recommend CEV/CLV options for SW capabilities and development of
processes and toolshttp://menzies.us/pdf/06xomo202.pdf

[2] B. Boehm, E. Horowitz, R. Madachy, D. Reifer, B. K. Clark, B.
Steece, A. W. Brown, S. Chulani, and C. Abts. Software Cost Estimation
with Cocomo II. Prentice Hall, 2000

[3] R. Madachy. Heuristic risk assessment using cost factors. IEEE
Software, 14(3):51–59, May 1997

[4] T. Menzies and Y. Hu. Data mining for very busy people. In IEEE
Computer, November 2003. Available from
http://menzies.us/pdf/03tar2.pdf

[5] S. Kirkpatrick and C. D. Gelatt and M. P. Vecchi, Optimization by
Simulated Annealing, Science, Vol 220, Number 4598, pages 671-680,
1983.