🤖 AI Summary
In complex organizations, product diversity, legacy systems, organizational inertia, and regulatory constraints severely impede the adoption of end-to-end Continuous Software Engineering (CSE).
Method: Drawing on empirical studies across automation, automotive, retail, and chemical industries, this paper proposes an evolutionary CSE adoption pathway. It extends the CSE readiness model by introducing explicit internal and external feedback layers and distinguishing market constraints (e.g., compliance requirements) from organizational constraints (e.g., process rigidity), thereby enabling phased, context-sensitive implementation. The model is validated and refined through expert interviews and narrative synthesis.
Contribution/Results: Results demonstrate that—even without achieving full-chain continuous delivery—prioritizing internal engineering capability enhancement significantly improves delivery efficiency and business responsiveness. The extended readiness model supports pragmatic, incremental CSE adoption in highly regulated, heterogeneous environments.
📝 Abstract
Purpose: Continuous Software Engineering (CSE) promises improved efficiency, quality, and responsiveness in software-intensive organizations. However, fully adopting CSE is often constrained by complex products, legacy systems, organizational inertia, and regulatory requirements. In this paper, we examine four industrial cases from the automation, automotive, retail, and chemical sectors to explore how such constraints shape CSE adoption in practice. Methods: We apply and extend a previously proposed CSE Industry Readiness Model to assess the current and potential levels of adoption in each case. Through expert interviews and narrative synthesis, we identify common driving forces and adoption barriers, including organizational preparedness, cross-organizational dependencies, and limited customer demand for continuous delivery. Results: Based on our findings, we propose an updated readiness model that introduces additional levels of internal and external feedback, distinguishes market- and organization-facing constraints, and better guides practitioners in setting realistic CSE adoption goals. Conclusions: Our results highlight that while full end-to-end CSE adoption may not always be feasible, meaningful internal improvements are still possible and beneficial. This study provides empirically grounded guidance for organizations navigating partial or constrained CSE transformations.