Optimizing The Incremental Delivery Of Software Features Under Uncertainty

[Context] Lean and agile software development processes encourage delivering software in small increments so as to generate early business value, be able to adapt to changes, and reduce risks. Deciding what to build in each iteration is an important requirements engineering activity. The Incremental Funding Method (IFM) partly supports such decisions by identifying sequences of features delivery that optimize Net Present Value (NPV). [Problem] The IFM, however, does not deal explicitly with uncertainty and considers the maximization of NPV as the only objective, without explicit consideration for other objectives such as minimizing upfront investment costs and maximizing learning so as to reduce uncertainty and risk for future iterations. [Ideas] This short paper presents our ongoing research to address these limitations by extending IFM with Bayesian decision analysis to reason about uncertainty and with Pareto-based optimization to support decisions with respect multiple conflicting objectives. [Contributions] The paper presents the current version of our tool-supported extension of the IFM, illustrate it on a small example, and outlines our research agenda.