PRISM

This replication package supports the paper "Signal-Conditioned Prompting for LLM-Assisted Requirements Inspection". It contains two components: the PRISM signal computation pipeline and the PRISM-Copilot proof-of-concept experiment. Component 1 — PRISM Signal Pipeline (data/, validation/, anchors/) PRISM computes three requirement-level readiness signals — Structural Criticality (C), Linguistic Specificity (S), and Volatility Exposure (V) — over a 262-requirement corpus derived from the teraPROMISE NFR e-commerce subset. The pipeline runs in five reproducible steps: corpus cleaning (Step 1), sentence embedding (Step 2), redundancy resolution at τ = 0.86 (Step 3), signal scoring (Step 4), and Pareto front structuring (Step 5). All derived artifacts, embeddings, signal scores, and Pareto front assignments are provided under data/derived/. Validation experiments covering construct validity, behavioral plausibility, signal independence, and robustness under noise perturbation are reproducible via validation/run_validation.sh. Linguistic anchors used in S and V construction are externalized under anchors/ for transparency. Component 2 — PRISM-Copilot Experiment (experiments/prism_copilot_poc/) PRISM-Copilot instantiates the Signal-Conditioned Inspection Prompting (SCIP) pattern. The experiment evaluates 50 stratified requirements under three prompting conditions: no guidance (Condition A), signal-conditioned (Condition B), and generic heuristic (Condition C). The package includes the corpus master file with pre-computed signal vectors, the stratified evaluation sample, all three prompt templates, raw and parsed LLM outputs (GPT-4o, temperature 0), practitioner review sheets and responses from five independent reviewers, inter-rater agreement data, recall alignment results, and all analysis scripts (Steps 01–11). The signal-to-policy mapping protocol is documented in experiments/prism_copilot_poc/protocol/signal_policy_mapping.md. Reproducibility All paths are repository-relative. No absolute paths are used. Python 3.9+ is required with dependencies: numpy, pandas, scipy, networkx, scikit-learn. This package is anonymized for double-blind review. Author identifiers will be added upon acceptance.