Expert judgment is a common estimation approach in industry. However, there is very little research on the accuracy of expert judgment outside the area of effort estimation. In this paper, we present an industrial case study investigating subjective and objective measures of requirementss volatility. Data was collected in retrospect for all use cases of a medium-size software project. In addition, we determined subjective volatility by interviewing developers and managers of the project. Our data analysis show that structural measures perform better than expert judgment in estimating the total number of changes to use case based requirements. These results confirm results from a previous case study by the authors and suggest that project managers should not rely on expert judgment alone for decision making.

Requirements volatility is an important risk factor for software projects. Software measures can help in quantifying and predicting this risk. In this paper, we present an industrial case study that investigated measures of volatility for a medium size software project. The goal of the study was twofold: 1) to empirically validate a set of measures associated with the volatility of use case models (UCM); 2) to investigate the correlation between subjective and objective volatility. Measurement data was collected in retrospect for all use case models of the software project. In addition, we determined subjective volatility by interviewing stakeholders of the project. Our data analysis showed a high correlation between our measures of size of UCM and total number of changes, indicating that the measures of size of UCMs are good indicators of requirements volatility. No correlations was found between subjective and objective volatility. These results suggest that project managers at this company should measure their projects because of the risk to take wrong decisions based on their own and the developer's perceptions.

The goal of this research is to demonstrate that a subset of a set of 38 requirements management measures are good predictors of stability and volatility of requirements and change requests. At the time of writing we have theoretically validated ten of these 38 measures. We are currently planning and performing an industrial case study where we want to reach the goal described above.