  Maximizing satisfaction from offering features as part of the upcoming
release(s) is different from minimizing dissatisfaction gained from not
offering features. This asymmetric behavior has never been utilized for product
release planning. We study Asymmetric Release Planning (ARP) by accommodating
asymmetric feature evaluation. We formulated and solved ARP as a bi-criteria
optimization problem. In its essence, it is the search for optimized trade-offs
between maximum stakeholder satisfaction and minimum dissatisfaction. Different
techniques including a continuous variant of Kano analysis are available to
predict the impact on satisfaction and dissatisfaction with a product release
from offering or not offering a feature. As a proof of concept, we validated
the proposed solution approach called Satisfaction-Dissatisfaction Optimizer
(SDO) via a real-world case study project. From running three replications with
varying effort capacities, we demonstrate that SDO generates optimized
trade-off solutions being (i) of a different value profile and different
structure, (ii) superior to the application of random search and heuristics in
terms of quality and completeness, and (iii) superior to the usage of manually
generated solutions generated from managers of the case study company. A survey
with 20 stakeholders evaluated the applicability and usefulness of the
generated results.
