Low-Code Applications and the Limits of Auto-Composed Auditability

Abstract

Auto-composed low-code applications are increasingly adopted in enterprise environments to accelerate software delivery through abstraction and automation of development workflows. Existing research has primarily focused on performance, scalability, and usability aspects, while limited attention has been given to auditability challenges arising from implicit logic generation and dynamic execution behavior. A critical gap exists in understanding how abstraction layers, dependency chains, and adaptive workflows impact traceability and compliance verification in regulated settings. This study addresses this gap by systematically analyzing auditability degradation across workflow layers and complexity levels using structured evaluation parameters and heatmap-based visualization. The findings reveal that auditability loss is highly concentrated in orchestration and transformation layers and exhibits nonlinear escalation with increasing system complexity, leading to reduced transparency and unreliable trace reconstruction. The study highlights the necessity of designing audit-aware architectures and adaptive trace reconstruction mechanisms to ensure compliance and system accountability. These insights provide practical guidance for deploying low-code platforms in compliance-critical industries while maintaining reliability and governance standards.