This paper addresses the problem of requirements engineering for complex socio-technical systems where an optimal set of technology components and human operators have to be selected to achieve system goals. Goals are achieved by tasks that are expressed as operational scenarios with variations in environmental conditions. The approach taken is to develop a probabilistic model of system reliability as a Bavesian Belief Network (BBN). The BBN model predicts human and machine reliabilities, given input variables representing the scenario and ranges of environmental conditions (i.e weather, climate, etc.). A software tool, the System Reliability A na4vser (SRA) is described that runs a set of scenarios against the BBN models while systematically varying the ranges of 12 input variables specifying properties of human operators such as training, technical equipment specification and environmental conditions. The tool reports human and technical equipment specifications that "survive" the scenario testing at a reliability level higher than a userdefined level (e.g. failure probability p展开▼
