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\begin{document}

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\title{
Real-time Optimization of Requirements Models
}

\author{ Gregory Gay\inst{1}  \and
Tim Menzies\inst{1} \and 
Omid Jalali\inst{1} \and 
Martin Feather\inst{2} \and
James Kiper\inst{3}}
\authorrunning{Jalali et al.}
\institute{West Virginia University, Morgantown, WV, USA,\\
\email{gregoryg@csee.wvu.edu, tim@menzies.us, ojalali@mix.wvu.edu},\\ 
\and Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA, \\
\email{martin.s.feather@jpl.nasa.gov}
\and 
Dept. of Computer Science and Systems Analysis, Miami University, Oxford, OH, USA,\\
\email{kiperjd@muohio.edu }}

\maketitle
\begin{abstract}
Early life cycle risk models  
can represent the requirements that a development group would want to achieve, the risks that could prevent these requirements from being met, and mitigations that could alleviate those risks. 
Our task is the selection of the {\em least expensive} set
of mitigations that achieve the {\em highest attainment} of requirements.

As these risk models grow larger, the demand for faster optimization methods also
increases, particularly when those models are used by a large room of debating experts as part of rapid interactive
dialogues. 
Hence, there is a pressing need for 
``real-time requirements optimization''; i.e.
requirements optimizers  that can offer advice before
an expert's attention wanders to other issues.

One candidate technology for real-time requirements optimization is the KEYS2
search engine. KEYS2 uses
a very simple (hence, very fast) novel Bayesian technique that
identifies both the useful succinct sets of mitigations  
as well as cost-attainment tradeoffs for partial solutions. 
This paper reports experiments demonstrating that  KEYS2 
runs four orders of magnitude 
faster than our previous implementations and outperforms
standard search algorithms including a classic stochastic search
(simulated annealing), a state-of-the art local search (MaxWalkSat),
and a standard graph search (A*).

\end{abstract}

\end{document}
