Software requirements continue to evolve during application development to meet the changing needs of customers and market demands. Complementing current approaches that constrain the risk that changing requirements pose to project cost, schedule and quality, this research seeks to investigate the efficacy of Bayesian networks to predict levels of volatility early in the project lifecycle. A series of industrial empirical studies is undertaken to explore the causes and consequences of requirements change, the results of which inform prediction feasibility and model construction. Models are then validated using data from four projects in two industrial organisations. Results from empirical studies indicate that classification of changes according to the source of the change is practical and informative to decisions concerning requirements management and process selection. Changes coming from sources considered external to the project are more expensive and difficult to control by comparison to the more numerous changes that occur as result of adjustments to product direction, or requirement specification. Although certain requirements are more change prone than others, the relationship between volatility and requirement novelty and complexity is not straightforward. Bayesian network models to predict levels of requirement volatility constructed based upon these results perform better than project management estimations of volatility when models are trained from a project sharing industrial context. This research carries the implication that process selection should be based upon the types of changes likely, and that formal predictive models are a promising alternative to project management estimation when investment in data collection, re-use and learning is supported.