Determine optimal designs and human dimensions of adaptive AI-centric lifecycles

Determine the optimal designs and human dimensions of continuous, adaptive software-development lifecycles for data-intensive and AI-enabled systems, including their collaboration, explainability, skills, ethics, legacy-integration, and security requirements.

Background

The paper presents a five-layer adaptive lifecycle framework based on coupled artifact versioning, explicit contracts, statistical gates, closed-loop control, and governance by construction. It identifies the framework as conceptual rather than empirically validated and emphasizes that the field still lacks evidence about which lifecycle designs are optimal and how organizational, ethical, human, and operational constraints should shape those designs. These unresolved dimensions are also reflected in the research agenda’s calls for design-science evaluation, sustainability analysis, human-factors research, and educational research.

References

Beyond this single deployment, generalizing the Living Library model to other institutions raises questions this paper does not settle: how to standardize metadata across institutions with different cataloging histories; how to ensure long-term model accuracy and governance as underlying models change; what best practices should govern representing historical figures responsibly across different subjects and sensitivities; how to measure educational and engagement impact rather than infer it from anecdote; and what interoperability standards would let Layer 3 corpora from different institutions be queried together.

— The Living Library: Transforming Archival Collections into Conversational Knowledge Systems -- Lessons from the Theodore Roosevelt Presidential Library  (2609.09368 - Wang et al., 8 Sep 2026) in Section 10, “Limitations and Future Work,” paragraph “Open questions for the framework more broadly”

The literature establishes the reality and direction of this transformation beyond reasonable doubt, while leaving its measured magnitude, its optimal designs, and its human dimensions open---precisely the space in which the proposed research model and adaptive-lifecycle framework are positioned.

— Reshaping the SDLC for Data- and AI-Centric Systems  (2608.17824 - Alenezi, 18 Aug 2026) in Section 8, Conclusion

While prior work emphasizes that every change should be deployable and verifiable through automation , it remains an open question where autonomous refactoring should be placed within such pipelines, whether before commit, after integration, or as a parallel process.

— Continuous Autonomous Refactoring: A Research Roadmap for AI-Driven Code Quality Maintenance  (2609.01236 - Sun et al., 1 Sep 2026) in Section 3.6, Cross-Cutting and Practical Considerations, Integration into Continuous Delivery Pipelines

The contribution is theory construction and operationalization; empirical validity remains open to controlled, longitudinal, and field studies.

— Software Engineering in the Agent Era From Trustworthy Change to Human Agent Software Organizations  (2609.04630 - Wang et al., 4 Sep 2026) in Abstract; Section 1.4, “Theory Status, Scope, and Empirical Commitments”