A Hybrid Soft-Computing Framework for Uncertainty-Governed Interactive PLC Code Synthesis with Active Learning

This document provides the companion reproducibility package for the manuscript : A Hybrid Soft-Computing Framework for Uncertainty-Governed Interactive PLC Code Synthesis with Active Learning. It delivers (i) a complete implementation-level specification of the IPC+AL governance architecture and (ii) a transparent, task-scaled replication workflow for benchmarking uncertainty-governed synthesis against IPC and a baseline prompting suite including RaR, APE, AP, ReAct, and MTPR. To ensure independent reproducibility, the package consolidates full prompt specifications, hard-gate definitions, acquisition policies, and artifact schemas used in the study. Executable prompt instances are provided for two industrial case studies—Batch Mixing Process and Robot Pick-and-Place—across IEC~61131-3 dialects. By fixing the uncertainty governance protocol and varying only task instances, external researchers can deploy IPC and IPC+AL on alternative PLC benchmarks while preserving methodological consistency. The documentation further formalizes the end-to-end evaluation stack, including LLM-in-the-Loop validation with interchangeable validator committees and Human-in-the-Loop expert adjudication. Inter-rater reliability is computed at the gate-decision level using Cohen’s $\kappa$ and Krippendorff’s $\alpha$, strictly across tasks rather than across prompting methods, thereby preserving the rater–item structure required for statistically valid agreement estimation. Overall, this package functions as a standalone replication and audit manual. By externalizing uncertainty thresholds, acquisition rules, and fail-closed emission criteria, it enables regeneration of all intermediate artifacts, recomputation of statistical metrics, and verification that uncertainty-governed Active Learning consistently improves safety admissibility and reliability under controlled, auditable conditions.