def generate_metadata()

in security-policies/dev/generate_rule_metadata.py [0:0]

• 42 lines of code
• 5 McCabe index (conditional complexity)

def generate_metadata(benchmark_id: str, raw_data: pd.DataFrame, sections: dict):
    """
    Generate metadata for rules
    :param benchmark_id: Benchmark ID
    :param raw_data: ‘Raw’ data from the spreadsheet
    :param sections: Section metadata
    :return: List of Rule objects
    """
    normalized_data = replace_nan_with_empty_string(raw_data)
    metadata = []
    benchmark_tag = benchmark_id.removeprefix("cis_").upper() if benchmark_id != "cis_k8s" else f"Kubernetes"
    for rule in normalized_data.to_dict(orient="records"):
        # Check if rule was implemented
        if not rule_is_implemented(rule["Rule Number"], benchmark_id):
            continue

        benchmark_metadata = generate_rule_benchmark_metadata(
            benchmark_id,
            rule["Rule Number"],
        )
        r = Rule(
            id=str(
                uuid.uuid5(
                    uuid.NAMESPACE_DNS,
                    f"{benchmark_metadata.name} {rule['Title']} {rule['Rule Number']}",
                ),
            ),
            name=rule["Title"],
            profile_applicability=f"* {rule['profile_applicability']}",
            description=common.fix_code_blocks(rule["description"]),
            rationale=common.fix_code_blocks(rule.get("rationale", "")),
            audit=common.fix_code_blocks(rule.get("audit", "")),
            remediation=common.fix_code_blocks(rule.get("remediation", "")),
            impact=rule.get("impact", ""),
            default_value=rule.get(
                "default_value",
                read_existing_default_value(rule["Rule Number"], benchmark_id),
            ),
            references=parse_refs(rule.get("references", "")),
            section=sections[rule["Section"]],
            tags=[
                "CIS",
                benchmark_tag,
                f"CIS {rule['Rule Number']}",
                sections[rule["Section"]],
            ],
            version="1.0",
            benchmark=benchmark_metadata,
        )
        metadata.append(r)

    return metadata