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340 lines
14 KiB
Python
340 lines
14 KiB
Python
""" Collection of joins for the derived tables
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"""
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from . import snowflake_id
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import logging
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import numpy
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import pandas as pd
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import re
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from sarif_cli import hash
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from sarif_cli import status_writer
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class ZeroResults(Exception):
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pass
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#
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# Column types for scan-related pandas tables
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#
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class ScanTablesTypes:
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scans = {
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"id" : pd.UInt64Dtype(),
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"commit_id" : pd.StringDtype(),
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"project_id" : pd.UInt64Dtype(),
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"db_create_start" : numpy.dtype('M'),
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"db_create_stop" : numpy.dtype('M'),
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"scan_start_date" : numpy.dtype('M'),
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"scan_stop_date" : numpy.dtype('M'),
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"tool_name" : pd.StringDtype(),
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"tool_version" : pd.StringDtype(),
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"tool_query_commit_id" : pd.StringDtype(),
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"sarif_file_name" : pd.StringDtype(),
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"results_count" : pd.Int64Dtype(),
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"rules_count" : pd.Int64Dtype(),
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}
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results = {
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'id' : pd.UInt64Dtype(),
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'scan_id' : pd.UInt64Dtype(),
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'query_id' : pd.StringDtype(),
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'query_kind' : pd.StringDtype(),
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'query_precision' : pd.StringDtype(),
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'query_severity' : pd.StringDtype(),
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'query_tags' : pd.StringDtype(),
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'codeFlow_id' : pd.UInt64Dtype(),
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'message' : pd.StringDtype(),
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'message_object' : numpy.dtype('O'),
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'location' : pd.StringDtype(),
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'source_location' : pd.StringDtype(),
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'source_startLine' : pd.Int64Dtype(),
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'source_startCol' : pd.Int64Dtype(),
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'source_endLine' : pd.Int64Dtype(),
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'source_endCol' : pd.Int64Dtype(),
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'sink_location' : pd.StringDtype(),
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'sink_startLine' : pd.Int64Dtype(),
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'sink_startCol' : pd.Int64Dtype(),
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'sink_endLine' : pd.Int64Dtype(),
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'sink_endCol' : pd.Int64Dtype(),
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# TODO Find high-level info from query name or tags?
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'source_object' : numpy.dtype('O'),
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'sink_object' : numpy.dtype('O'),
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}
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projects = {
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"id" : pd.UInt64Dtype(),
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"project_name" : pd.StringDtype(),
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"creation_date" : numpy.dtype('M'),
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"repo_url" : pd.StringDtype(),
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"primary_language" : pd.StringDtype(),
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"languages_analyzed" : pd.StringDtype(),
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# "automationDetails" : pd.StringDtype(),
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}
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#
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# Projects table
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#
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def joins_for_projects(basetables, external_info):
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"""
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Form the 'projects' table for the ScanTables dataclass
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"""
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b = basetables; e = external_info
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extra = ""
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# if the sarif does have automationDetails
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if "automationDetails" in b.project:
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extra = b.project.automationDetails[0]
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# if the sarif does have versionControlProvenance
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if "repositoryUri" in b.project:
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repoUri = b.project.repositoryUri[0]
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e.project_id = hash.hash_unique((repoUri+extra).encode())
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else:
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repoUri = "unknown"
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res = pd.DataFrame(data={
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"id" : e.project_id,
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"project_name" : repoUri,
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"creation_date" : pd.Timestamp(0.0, unit='s'), # TODO: external info
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"repo_url" : repoUri,
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"primary_language" : b.project['semmle.sourceLanguage'][0],
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"languages_analyzed" : ",".join(list(b.project['semmle.sourceLanguage'])),
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"automationDetails" : extra,
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}, index=[0])
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# Force all column types to ensure appropriate formatting
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res1 = res.astype(ScanTablesTypes.projects).reset_index(drop=True)
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#
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return res1
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#
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# Scans table
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#
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def joins_for_scans(basetables, external_info, scantables, sarif_type):
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"""
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Form the `scans` table for the ScanTables dataclass
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"""
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b = basetables; e = external_info
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driver_name = b.project.driver_name.unique()
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assert len(driver_name) == 1, "More than one driver name found for single sarif file."
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driver_version = b.project.driver_version.unique()
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assert len(driver_version) == 1, \
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"More than one driver version found for single sarif file."
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# TODO if commit id exists in external info for CLI gen'd sarif, add?
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if sarif_type == "LGTM":
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commit_id = b.project.revisionId[0]
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else:
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commit_id = "unknown"
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res = pd.DataFrame(data={
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"id" : e.scan_id,
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"commit_id" : commit_id,
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"project_id" : e.project_id,
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# TODO extract real date information from somewhere external
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"db_create_start" : pd.Timestamp(0.0, unit='s'),
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"db_create_stop" : pd.Timestamp(0.0, unit='s'),
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"scan_start_date" : pd.Timestamp(0.0, unit='s'),
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"scan_stop_date" : pd.Timestamp(0.0, unit='s'),
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#
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"tool_name" : driver_name[0],
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"tool_version" : driver_version[0],
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"tool_query_commit_id" : pd.NA,
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"sarif_file_name" : e.sarif_file_name,
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"results_count" : scantables.results.shape[0],
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"rules_count" : len(b.rules['id'].unique()),
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},index=[0])
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# Force all column types to ensure correct writing and type checks on reading.
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res1 = res.astype(ScanTablesTypes.scans).reset_index(drop=True)
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return res1
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#
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# Results table
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#
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def joins_for_results(basetables, external_info):
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"""
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Form and return the `results` table
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"""
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# Get one table per query_kind, then stack them,
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# problem
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# path-problem
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#
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# Concatenation with an empty table triggers type conversion to float, so don't
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# include empty tables.
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tables = [_results_from_kind_problem(basetables, external_info),
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_results_from_kind_pathproblem(basetables, external_info)]
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stack = [table for table in tables if len(table) > 0]
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# Concatenation fails without at least one table, so avoid that.
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if len(stack) > 0:
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res = pd.concat(stack)
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else:
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if stack == []:
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logging.warning("Zero problem/path_problem results found in sarif "
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"file but processing anyway.")
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status_writer.csv_write(status_writer.zero_results)
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res = tables[0]
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# Force all column types to ensure appropriate formatting
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res1 = res.astype(ScanTablesTypes.results).reset_index(drop=True)
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return res1
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#id as primary key
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def _populate_from_rule_table_code_flow_tag_text(basetable, flowtable):
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val = flowtable.rule_id.values[0]
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return basetable.rules.query("id == @val")["tag_text"].str.cat(sep='_')
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#id as primary key
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def _populate_from_rule_table_tag_text(basetable, i):
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val = basetable.kind_problem.rule_id[i]
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return basetable.rules.query("id == @val")["tag_text"].str.cat(sep='_')
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#id as primary key
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def _populate_from_rule_table(column_name, basetable, i):
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val = basetable.kind_problem.rule_id[i]
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return basetable.rules.query("id == @val")[column_name].head(1).item()
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#id as primary key
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def _populate_from_rule_table_code_flow(column_name, basetable, flowtable):
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val = flowtable.rule_id.values[0]
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return basetable.rules.query("id == @val")[column_name].head(1).item()
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def _results_from_kind_problem(basetables, external_info):
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b = basetables; e = external_info
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flakegen = snowflake_id.Snowflake(2)
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res = pd.DataFrame(
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data={
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'id': [flakegen.next() for _ in range(len(b.kind_problem))],
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'scan_id' : e.scan_id,
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'query_id' : b.kind_problem.rule_id,
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'query_kind' : "problem",
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'query_precision' : [_populate_from_rule_table("precision", b, i) for i in range(len(b.kind_problem))],
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'query_severity' : [_populate_from_rule_table("problem.severity", b, i) for i in range(len(b.kind_problem))],
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'query_tags' : [_populate_from_rule_table_tag_text(b, i) for i in range(len(b.kind_problem))],
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'codeFlow_id' : 0, # link to codeflows (kind_pathproblem only, NULL here)
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'message': b.kind_problem.message_text,
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'message_object' : pd.NA,
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'location': b.kind_problem.location_uri,
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# for kind_problem, use the same location for source and sink
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'source_location' : pd.NA,
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'source_startLine' : b.kind_problem.location_startLine,
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'source_startCol' : b.kind_problem.location_startColumn,
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'source_endLine' : b.kind_problem.location_endLine,
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'source_endCol' : b.kind_problem.location_endColumn,
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'sink_location' : pd.NA,
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'sink_startLine' : b.kind_problem.location_startLine,
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'sink_startCol' : b.kind_problem.location_startColumn,
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'sink_endLine' : b.kind_problem.location_endLine,
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'sink_endCol' : b.kind_problem.location_endColumn,
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'source_object' : pd.NA, # TODO: find high-level info from query name or tags?
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'sink_object' : pd.NA,
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})
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# Force column type(s) to avoid floats in output.
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res1 = res.astype({ 'id' : 'uint64', 'scan_id': 'uint64'}).reset_index(drop=True)
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return res1
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def _results_from_kind_pathproblem(basetables, external_info):
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#
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# Only get source and sink, no paths. This implies one codeflow_index and one
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# threadflow_index, no repetitions.
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#
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b = basetables; e = external_info
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flakegen = snowflake_id.Snowflake(3)
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# The sarif tables have relatedLocation information, which result in multiple
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# results for a single codeFlows_id -- the expression
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# b.kind_pathproblem[b.kind_pathproblem['codeFlows_id'] == cfid0]
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# produces multiple rows.
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#
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# The `result` table has no entry to distinguish these, so we use a simplified
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# version of `kind_pathproblem`.
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reduced_kind_pathp = b.kind_pathproblem.drop(
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columns=[
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'relatedLocation_array_index',
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'relatedLocation_endColumn',
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'relatedLocation_endLine',
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'relatedLocation_id',
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'relatedLocation_index',
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'relatedLocation_message',
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'relatedLocation_startColumn',
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'relatedLocation_startLine',
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'relatedLocation_uri',
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'relatedLocation_uriBaseId',
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])
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# Per codeflow_id taken from b.kind_pathproblem table, it should suffice to
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# take one codeflow_index, one threadflow_index, first and last location_index
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# from the b.codeflows table.
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#
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# To ensure nothing is missed, collect all the entries and then check for
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# unique rows.
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cfids = reduced_kind_pathp['codeFlows_id'].unique()
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source_sink_coll = []
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for cfid0 in cfids:
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cfid0t0 = b.codeflows[b.codeflows['codeflow_id'] == cfid0]
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cfid0ppt0 = reduced_kind_pathp[reduced_kind_pathp['codeFlows_id'] ==
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cfid0].drop_duplicates()
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assert cfid0ppt0.shape[0] == 1, \
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"Reduced kind_pathproblem table still has multiple entries"
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for cfi0 in range(0, cfid0t0['codeflow_index'].max()+1):
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cf0 = cfid0t0[cfid0t0['codeflow_index'] == cfi0]
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for tfi0 in range(0, cf0['threadflow_index'].max()+1):
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tf0 = cf0[ cf0['threadflow_index'] == tfi0 ]
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loc_first = tf0['location_index'].min()
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loc_last = tf0['location_index'].max()
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source = tf0[tf0['location_index'] == loc_first]
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sink = tf0[tf0['location_index'] == loc_last]
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# Note that we're adding the unique row ids after the full table
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# is done, below.
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res = {
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'scan_id' : e.scan_id,
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'query_id' : cfid0ppt0.rule_id.values[0],
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'query_kind' : "path-problem",
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'query_precision' : _populate_from_rule_table_code_flow("precision", b, cfid0ppt0),
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'query_severity' : _populate_from_rule_table_code_flow("problem.severity", b, cfid0ppt0),
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'query_tags' : _populate_from_rule_table_code_flow_tag_text(b, cfid0ppt0),
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'codeFlow_id' : cfid0,
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#
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'message': cfid0ppt0.message_text.values[0],
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'message_object' : pd.NA,
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'location': cfid0ppt0.location_uri.values[0],
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#
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'source_location' : source.uri.values[0],
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'source_startLine' : source.startLine.values[0],
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'source_startCol' : source.startColumn.values[0],
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'source_endLine' : source.endLine.values[0],
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'source_endCol' : source.endColumn.values[0],
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#
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'sink_location' : sink.uri.values[0],
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'sink_startLine' : sink.startLine.values[0],
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'sink_startCol' : sink.startColumn.values[0],
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'sink_endLine' : sink.endLine.values[0],
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'sink_endCol' : sink.endColumn.values[0],
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#
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'source_object' : pd.NA, # TODO: find high-level info from
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# query name or tags?
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'sink_object' : pd.NA,
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}
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source_sink_coll.append(res)
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results0 = pd.DataFrame(data=source_sink_coll).drop_duplicates().reset_index(drop=True)
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# Add the snowflake ids
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results0['id'] = [flakegen.next() for _ in range(len(results0))]
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# The 'scan_id' column is needed for astype
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if len(results0) == 0:
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results0['scan_id'] = []
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# Force column type(s) to avoid floats in output.
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results1 = results0.astype({ 'id' : 'uint64', 'scan_id': 'uint64'}).reset_index(drop=True)
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return results1
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