Abstract
Choosing how to write natural language scenarios is challenging, because stakeholders may over-generalize their descriptions or overlook or be unaware of alternate scenarios. In security, for example, this can result in weak security constraints that are too general, or missing constraints. Another challenge is that analysts are unclear on where to stop generating new scenarios. In this paper, we introduce the Multifactor Quality Method (MQM) to help requirements analysts to empirically collect system constraints in scenarios based on elicited expert preferences. The method combines quantitative statistical analysis to measure system quality with qualitative coding to extract new requirements. The method is bootstrapped with minimal analyst expertise in the domain affected by the quality area, and then guides an analyst toward selecting expert-recommended requirements to monotonically increase system quality. We report the results of applying the method to security. This include 550 requirements elicited from 69 security experts during a bootstrapping stage, and subsequent evaluation of these results in a verification stage with 45 security experts to measure the overall improvement of the new requirements. Security experts in our studies have an average of 10 years of experience. Our results show that using our method, we detect an increase in the security quality ratings collected in the verification stage. Finally, we discuss how our proposed method helps to improve security requirements elicitation, analysis, and measurement.