Abstract
Early life cycle risk models can represent the requirements that a de-
velopment group would want to achieve, the risks that could prevent these re-
quirements from being met, and mitigations that could alleviate those risks. Our
task is the selection of the least expensive set of mitigations that achieve the high-
est 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 tech-
nique that identifies both the useful succinct sets of mitigations as well as cost-
attainment tradeoffs for partial solutions. This paper reports experiments demon-
strating that KEYS2 runs four orders of magnitude faster than our previous im-
plementations and outperforms standard search algorithms including a classic
stochastic search (simulated annealing), a state-of-the art local search (MaxWalk-
Sat), and a standard graph search (A*)
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