Supplementary Material: SLR Pool for "What Deployers of AI Coding Need to Know?"

This archive contains the supplementary material for the ICEGOV 2026Submission: "What Deployers of AI Coding Need to Know? Mapping LLM-BasedCode Risks to Governance Gaps." The material is shared anonymously fordouble-blind peer review. Contents:- Peer_Reviewed.csv: 113 peer-reviewed papers from the systematic literature review, with bibliographic metadata, risk category assignments, and classification rationale.- arXiv_Papers.csv: 283 arXiv preprints processed under the same classification pipeline, used to assess evidence maturity and emerging risk trajectories. The full pool (396 papers) was retrieved from the DBLP Knowledge Graphvia nine SPARQL queries targeting nine risk categories of LLM-based codegeneration. Each paper was classified into one or more categories usingan LLM-assisted pipeline, validated against a stratified manual sample.The CSVs support replication of all counts, ratios, and co-occurrencestatistics reported in the paper.