An architecture governance approach for Agile development by tailoring the Spotify model

Salameh, A; Bass, J

Abstract

The role of software architecture in large-scale Agile development is important because several teams need to work together to release a single software product while helping to maximise teams’ autonomy. Governing and aligning Agile architecture across autonomous squads (i.e., teams), when using the Spotify model, is a challenge because the Spotify model lacks practices for addressing Agile architecture governance. To explore how software architecture can be governed and aligned by scaling the Spotify model, we conducted a longitudinal embedded case study in a multinational FinTech organisation. Then, we developed and evaluated an approach for architectural governance by conducting an embedded case study. The collected data was analysed using Thematic Analysis and informed by selected Grounded Theory techniques such as memoing, open coding, constant comparison, and sorting. Our approach for architectural governance comprises an organisational structure change and an architecture change management process. The benefits reported by the practitioners include devolving architectural decision-making to the operational level (i.e., Architecture Owners), enhancing architectural knowledge sharing among squads, minimising wasted effort in architectural refactoring, and other benefits. The practitioners in our case study realised an improved squad autonomy by the ability to govern and align architectural decisions. We provide two key contributions in this paper. First, we present the characteristics of our proposed architectural governance approach, its evaluation, benefits, and challenges. Second, we present how the novel Heterogeneous Tailoring model was enhanced to accommodate our architectural governance approach.

Citation

Salameh, A., & Bass, J. (2021). An architecture governance approach for Agile development by tailoring the Spotify model. AI & society, 37(2), 761-780. https://doi.org/10.1007/s00146-021-01240-x

• Journal Article Type: Article
• Acceptance Date: Jun 1, 2021
• Online Publication Date: Jun 24, 2021
• Publication Date: Jun 24, 2021
• Deposit Date: Jul 1, 2021
• Publicly Available Date: Jul 1, 2021
• Journal: AI and Society
• Print ISSN: 0951-5666
• Electronic ISSN: 1435-5655
• Publisher: Springer Verlag
• Volume: 37
• Issue: 2
• Pages: 761-780
• DOI: https://doi.org/10.1007/s00146-021-01240-x
• Publisher URL: https://doi.org/10.1007/s00146-021-01240-x
• Related Public URLs: http://link.springer.com/journal/146