%0 Journal Article %1 gay08tse %A Gay, Gregory %A Menzies, Tim %A Jalali, Omid %A Feather, Martin %A Kiper, James %D 2008 %K optimization requirements %T Real-time Optimization of Requirements Models %X 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*)

@article{gay08tse, 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*)}, added-at = {2008-10-16T23:41:51.000+0200}, author = {Gay, Gregory and Menzies, Tim and Jalali, Omid and Feather, Martin and Kiper, James}, biburl = {https://www.bibsonomy.org/bibtex/2beb5d1d3e7007863103905ece638c305/neilernst}, interhash = {a2d7594000d3cf7dc44a9b7017ee5f4c}, intrahash = {beb5d1d3e7007863103905ece638c305}, keywords = {optimization requirements}, note = {in press}, timestamp = {2008-10-16T23:41:51.000+0200}, title = {Real-time Optimization of Requirements Models}, year = 2008 }