QREME - Quality Requirements Management Model for Supporting Decision-Making
This addresses the problem of timely and effective decision-making in product development for companies dealing with subjective and context-dependent quality requirements, though it appears incremental as it integrates existing methodologies.
The paper tackles the challenge of managing quality requirements (QRs) by proposing a model that combines data-driven, bottom-up approaches with established top-down methods, based on a five-year empirical investigation at a large B2C company and illustrated through two industrial case studies.
[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.