Abstract
Release planning is a mandatory part of incremental and iterative software development. For the decision about which features should be implemented next, the values of features need to be balanced with the effort and readiness of their implementation. Traditional planning looks at the sum of the values of individual and potentially isolated features. As an alternative idea, a theme is a meta-functionality which integrates a number of individual features under a joint umbrella. That way, possible value synergies from offering features in conjunction (theme-related) can be utilized.
In this paper, we model theme-based release planning as a bi-objective (search-based) optimization problem. Each solution of this optimization problem balances the preference between individual and theme-based planning objectives. We apply a two-stage solution approach. In Phase 1, the existing Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is adapted. Subsequently, the problem of guiding the user in the set of non-dominated solutions is addressed in Phase 2. We propose and explore two alternative ways to select among the (potentially large number) Pareto-optimal solutions. The applicability and empirical analysis of the proposed approach is evaluated for two explorative case study projects having 50 resp. 25 features grouped around 8 resp. 5 themes.