Quantify the causal effects of data-engineering and software-engineering integration

Quantify the causal effect of integrating data-engineering and software-engineering practices on the efficiency, quality, reliability, and maintainability of data-intensive and AI-enabled systems.

Background

The paper argues that existing mapping studies, surveys, interviews, and industrial reports consistently establish the direction of SDLC transformation toward integrated DataOps, MLOps, and AI-centric practices. However, the available evidence contains few comparable experiments, participant-level statistics, or pooled effect sizes. Consequently, the magnitude of the benefits attributable to data-engineering and software-engineering integration has not been adequately measured, motivating empirical variance studies and process studies based on the paper’s conceptual research model.

References

The direction of the transformation is therefore well established while its magnitude---the causal effect of DE--SE integration on efficiency, quality, reliability, and maintainability---remains insufficiently quantified.

— Reshaping the SDLC for Data- and AI-Centric Systems  (2608.17824 - Alenezi, 18 Aug 2026) in Section 7.1, “What the Evidence Establishes—and What It Does Not”