Converging on the optimal attainment of requirements

Planning for the optimal attainment of requirements is an important early lifecycle activity. However, such planning is difficult when dealing with competing requirements, limited resources, and the incompleteness of information available at requirements time. A novel approach to requirements optimization is described. A requirements interaction model is executed to randomly sample the space of options. This produces a large amount of data, which is then condensed by a summarization tool. The result is a small list of critical decisions (i.e., those most influential in leading towards the desired optimum). This focuses human experts´ attention on a relatively few decisions and makes them aware of major alternatives. This approach is iterative. Each iteration allows experts to select from among the major alternatives. In successive iterations the execution and summarization modules are run again, but each time further constrained by the decisions made in previous iteration. In the case study shown here, out of 99 yes/no decisions (approximately 10³⁰ possibilities), five iterations were sufficient to find and make the 30 key ones.