QREME - Quality Requirements Management Model for Supporting Decision-Making

TL;DR

This paper introduces QREME, a model that integrates top-down management and bottom-up data-driven approaches to improve decision-making in quality requirements management.

Contribution

The paper presents a novel model for managing quality requirements by combining empirical decision-making insights with industrial case studies.

Findings

Empirically derived model from five years of decision data.

Successful application in two industrial case studies.

Highlights importance of context-specific approaches for QRs.

Abstract

[Context and motivation] Quality requirements (QRs) are inherently diffi-cult to manage as they are often subjective, context-dependent and hard to fully grasp by various stakeholders. Furthermore, there are many sources that can provide input on important QRs and suitable levels. Responding timely to customer needs and realizing them in product portfolio and product scope decisions remain the main challenge. [Question/problem] Data-driven methodologies based on product usage data analysis gain popularity and enable new (bottom-up, feedback-driven) ways of planning and evaluating QRs in product development. Can these be effi-ciently combined with established top-down, forward-driven management of QRs? [Principal idea / Results] We propose a model for how to handle decisions about QRs at a strategic and operational level, encompassing product deci-sions as well as business intelligence and usage data. We inferred the model from an extensive empirical investigation of five years of decision making history at a large B2C company. We illustrate the model by assessing two in-dustrial case studies from different domains. [Contribution] We believe that utilizing the right approach in the right situa-tion will be key for handling QRs, as both different groups of QRs and do-mains have their special characteristics.