Asymmetric Release Planning-Compromising Satisfaction against Dissatisfaction

TL;DR

This paper introduces Asymmetric Release Planning (ARP), a novel bi-criteria optimization approach that balances stakeholder satisfaction and dissatisfaction, validated through a real-world case study and stakeholder survey.

Contribution

It formulates ARP as a bi-criteria optimization problem and proposes the Satisfaction-Dissatisfaction Optimizer (SDO), a new method for product release planning considering asymmetric stakeholder evaluations.

Findings

SDO produces optimized solutions with distinct value profiles.

SDO outperforms random search and heuristics in quality and completeness.

Stakeholders find the approach applicable and useful.

Abstract

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.