Search based data sensitivity analysis applied to requirement engineering

Software engineering is plagued by problems associated with unreliable cost estimates. This paper introduces an approach to sensitivity analysis for requirements engineering. It uses Search-Based Software Engineering to aid the decision maker to explore sensitivity of the cost estimates of requirements for the Next Release Problem (NRP). The paper presents both single- and multi-objective formulation of NRP with empirical sensitivity analysis on synthetic and real-world data. The results show strong correlation between the level of inaccuracy and the impact on the selection of requirements, as well as between the cost of requirements and the impact, which is as intuitively expected. However, there also exist a few sensitive exceptions to these trends; the paper uses a heat-map style visualisation to reveal these exceptions which require careful consideration. The paper also shows that such unusually sensitivity patterns occur in real-world data and how the proposed approach clearly identifies them.