Abstract
Context: Requirements engineering (RE) literature acknowledges the importance of early stakeholder identification. The
sources of requirements are many and also constantly changing as the market and business constantly change.
Identifying and consulting all stakeholders on the market is impractical; thus many companies utilize indirect data sources, e.g.
documents and representatives of larger groups of stakeholders. However, companies often collect irrelevant data or develop their
products based on the sub-optimal information sources that may lead to missing market opportunities.
Objectives: We propose a collaborative method for identification and selection of data sources. The method consists of four
steps and aims to build consensus between different perspectives in an organization.
Methods: We develop the method following the design science research method. We demonstrate the use of the method with
three industrial case studies.
Results: Our results show that the method can support the identification and selection of data sources in three ways: (1) by
providing systematic steps to identify and prioritize data sources for RE, (2) by highlighting and resolving discrepancies between
different perspectives in an organization, and (3) by analyzing the underlying rationale for using certain data sources.
Conclusion: We conclude that our proposed method is well suited to support systematic identification of requirements sources
in industry. Further work on the method include validation and adaptation for use different contexts, and developing tool support.
sources of requirements are many and also constantly changing as the market and business constantly change.
Identifying and consulting all stakeholders on the market is impractical; thus many companies utilize indirect data sources, e.g.
documents and representatives of larger groups of stakeholders. However, companies often collect irrelevant data or develop their
products based on the sub-optimal information sources that may lead to missing market opportunities.
Objectives: We propose a collaborative method for identification and selection of data sources. The method consists of four
steps and aims to build consensus between different perspectives in an organization.
Methods: We develop the method following the design science research method. We demonstrate the use of the method with
three industrial case studies.
Results: Our results show that the method can support the identification and selection of data sources in three ways: (1) by
providing systematic steps to identify and prioritize data sources for RE, (2) by highlighting and resolving discrepancies between
different perspectives in an organization, and (3) by analyzing the underlying rationale for using certain data sources.
Conclusion: We conclude that our proposed method is well suited to support systematic identification of requirements sources
in industry. Further work on the method include validation and adaptation for use different contexts, and developing tool support.
| Original language | English |
|---|---|
| Publisher | SSRN |
| Number of pages | 25 |
| Publication status | Published - 2022 |