Coverage — Per-File Table (CSV)
The same coverage as a flat table with a TOTAL row, for spreadsheets, trend charts and diffing two runs without an XML parser. The TOTAL row is the arithmetic sum of the four file rows, so it doubles as a checksum on any tool that regenerates it. Every file in this group describes the same four-file source tree and reports 127/140 lines, 17/24 branch outcomes and 18/20 functions, so a converter can be diffed against a known answer.
file,lines_found,lines_hit,line_pct,functions_found,functions_hit,branches_found,branches_hit,branch_pct
src/cart/pricing.js,44,40,90.91,6,5,8,6,75.00
src/cart/discounts.js,33,28,84.85,5,4,6,4,66.67
src/checkout/session.js,39,36,92.31,6,6,6,4,66.67
src/checkout/receipt.js,24,23,95.83,3,3,4,3,75.00
TOTAL,140,127,90.71,20,18,24,17,70.83
Specifications
- Seed
- 61200
- Source Files
- 4
- Lines Found
- 140
- Lines Hit
- 127
- Line Pct
- 90.71
- Branches Found
- 24
- Branches Hit
- 17
- Branch Pct
- 70.83
- Functions Found
- 20
- Functions Hit
- 18
- Line Endings
- LF
- Format
- CSV
- Rows
- 5
- Columns
- 9
- Delimiter
- ,
- Header
- true
- Has Total Row
- true
Testing contract
Expected to pass- Scenario
- Recompute overall coverage from a per-file CSV and check the TOTAL row.
- Expected result
- Summing lines_hit over the four file rows gives 127, equal to the TOTAL row.
What is a .csv file?
CSV (Comma-Separated Values) is a plain-text tabular format where rows are lines and fields are separated by commas, with quoting rules for values that contain delimiters, quotes, or newlines. It has no formal type system and depends on encoding and dialect conventions. It is the most portable format for tabular data exchange.
How to use this file
Use an example CSV to test parsers against quoting and embedded-delimiter edge cases, header handling, encoding detection, and import pipelines into databases or spreadsheets.
How to use this file for testing
“Coverage — Per-File Table (CSV)” is a deterministic Novus Examples fixture for CSV parsing, Conversion testing, Data import. Clean and deliberately messy CSVs — quoted commas, embedded newlines, ragged rows, odd delimiters, and encodings.
Documented properties for this file: seed 61200 · 5 rows · 9 columns · LF. Compare results against paired or grouped companions on this page when present (clean↔damaged, searchable↔scanned, or format twins) so scores stay reproducible across runs.
Download the file once, keep the path stable in CI or local scripts, and treat the spec table as the contract: dimensions, seeds, field lists, and roles are intentional. Corrupt or invalid samples are labelled as such — expect parsers to fail loudly rather than silently accept them.
Test and coverage reports document their totals (suites, cases, passes, failures, skips, covered lines) in the spec table. Point your CI dashboard, coverage gate, or report converter at the file and assert those counts survive; format twins carry identical numbers so a conversion can be scored exactly.
Feed the file to your parser and assert it handles the documented quirks — quoted delimiters, embedded newlines, ragged rows, or invalid syntax; the valid↔invalid distinction is labelled in the title.
Code examples
import pandas as pd
df = pd.read_csv("coverage-by-file.csv")
print(df.head())
print(df.dtypes)Related files
- csvTest Case Matrix — Twelve Documented Cases (CSV)Twelve test cases with level, priority, the requirement each covers, preconditions, steps and expected result — the table a test-management import expects. Eleven of the twelve are automated, and the requirement column joins this file to the traceability matrix next to it.
- csvLockfile Pin Summary (CSV)Declared range against resolved version for every fictional package pinned by the lockfiles in this category — the table a review needs to see which pins drifted from their manifest ranges. Every package, version, hash and licence is fictional — the tree describes nothing real.
- csvSBOM Component Inventory (CSV)The same fictional component tree flattened to a spreadsheet-friendly CSV — name, version, licence, depth, purl and a SHA-256 prefix — for testing the export path SBOM tools offer alongside JSON. Every package, version, hash and licence is fictional — the tree describes nothing real.
- csvVulnerability Summary (CSV)The four SAMPLE advisories flattened to one row each — the export a security review circulates, and the reference answer for any converter run against the Trivy, Grype or OSV reports here. Advisory identifiers use the invented NOVUS-SAMPLE namespace with SAMPLE-CVE aliases; no identifier here refers to a published CVE, GHSA or OSV record, and no package named exists.
- csvFlaky Detection — Per-Test Failure-Rate Summary (CSV)The aggregate answer key for the 40-build history in this group: runs, passes, failures and failure rate per test, with each test labelled stable or flaky. Use it to score a detector rather than eyeballing 240 JSONL records.
- csvFlaky Run — Network Dependency, Flake-Rate Table (CSV)The answer key for the network dependency family: the flake rate a detector should derive from the five build reports next to it. Exactly one of the six tests is unstable, at a rate of 2/5; the other five are stable at 0.00.
Generated by generation/testing.py. Free for any use, no attribution required — license.