Asymmetric Release Planning: Compromising Satisfaction against Dissatisfaction

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 bicriteria 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.

Automatically Matching Bug Reports With Related App Reviews

“…Further studies [23], [24] looked at the classified feedback more closely by analyzing and understanding user rationale-the reasoning and justification of user decisions, opinions, and beliefs. Once a company decides to integrate, for example, an innovative feature request in the software product, it w ill be forwarded to the release planning phase [35], [36].…”

A Phrase-Level User Requests Mining Approach in Mobile Application Reviews: Concept, Framework, and Operation

“…Nayebi and Ruhe [12] calculated the optimized functionality for a certain app category. Nayebi and Ruhe [22] proposed an asymmetric release planning to maximize satisfaction and minimize dissatisfaction by predicting whether an app feature should be offered or not in the next release. These two studies both use bi-criterion integer programming to solve the trade-off between feature values and costs or satisfaction and dissatisfaction.…”

Automatically Matching Bug Reports With Related App Reviews

“…Further studies [23], [24] looked at the classified feedback more closely by analyzing and understanding user rationale-the reasoning and justification of user decisions, opinions, and beliefs. Once a company decides to integrate, for example, an innovative feature request in the software product, it will be forwarded to the release planning phase [35], [36].…”