Testcase Importer

Test-case importer

Turn messy, foreign-formatted test-case data into the project's canonical CSV (the 10 columns the HTML console reads) and render it. The hard parts — parsing odd formats, matching column names, normalizing vocab, generating IDs, validating — are done by the bundled script. The judgment part — inferring the tag fields a source didn't provide — is yours, guided by the taxonomy, because a keyword script guesses those badly and silently.

The canonical schema, vocab, and the rules for inferring missing fields live in references/schema.md. Read it before filling any gaps.

Workflow

1 — Locate the input

A file path (.xlsx/.xls/.csv/.tsv/.pdf/.md) or a table pasted into chat. For a pasted markdown/table, save it to a .md file or pipe it via stdin (... | ... -).

2 — Run the normalizer

python scripts/normalize_testcases.py <INPUT> --name <NAME>

It writes <NAME>-testcases.csv (canonical columns, blanks where it couldn't decide) and <NAME>-import-gaps.json, and prints a summary: rows parsed, which columns mapped, which source columns were dropped (unmapped), and which fields still need work.

• PDF needs pdfplumber (pip install pdfplumber); if absent the script says so — ask the user to install it or export the table to CSV/XLSX.
• Old .xls uses xlrd; .xlsx uses openpyxl/pandas (already available).

3 — Read the gaps report and the dropped columns

Open <NAME>-import-gaps.json. Two things matter:

• unmapped_source_headers — columns the script couldn't place. If one clearly holds a canonical field the script missed, fix the CSV (or tell the user it was dropped and why).
• rows_needing_work — each row's blank inferable fields (Type/Outcome/Priority/ Severity_Reasoning) and any content_missing fields (Title/Steps/Expected).

4 — Fill the inferable gaps (judgment)

For every flagged row, reason from its Title + Steps + Expected and set the blank fields per references/schema.md:

• Type POS/NEG/VAPT · Outcome TN/TP/FP/FN! · Priority on the scale matching Type (functional P0/P1/P2, VAPT CRIT/HIGH/MED/LOW) · Severity_Reasoning one sentence on blast radius · Transition only if states are described, else leave -.

Edit <NAME>-testcases.csv directly to fill the blanks (it's already the right shape). Keep every value inside the canonical vocab.

Content gaps (a blank Title/Steps/Expected — the source was incomplete): do not invent test content. Leave blank, and list the row for the user.

5 — Write the review sidecar

Create <NAME>-import-review.md listing the rows where you inferred a field that the source didn't clearly imply (especially any FN!/CRIT you assigned), one line each on why it's uncertain, plus any dropped columns and content gaps. This is what the user sanity-checks.

6 — Validate and render

python -c "import csv; rows=list(csv.DictReader(open('<NAME>-testcases.csv'))); \
  assert rows and not [c for c in ['ID','Group','Type','Outcome','Priority','Severity_Reasoning','Transition','Title','Steps / Test Data','Expected Result + Downstream Impact / Fix'] if c not in rows[0]]; print(len(rows),'rows ok')"
python scripts/render_console.py --csv <NAME>-testcases.csv --out <NAME>-testcases.html \
  --title "<Subject> · Test Matrix" --subtitle "<Subject>"

Open the HTML to confirm it renders (criticals/FN! show in red, facets populate, ? opens the legend).

7 — Report

Tell the user: rows imported, columns mapped vs dropped, how many fields you inferred, the review-sidecar path for the uncertain ones, any content gaps the source was missing, and the HTML path.

Examples

Example 1 — spreadsheet with foreign headers Input: qa_cases.xlsx with columns Test ID, Module, Scenario, Steps, Expected, Severity. → run normalizer → it maps those onto ID/Group/Title/Steps/Expected/Priority, leaves Type/Outcome/Severity_Reasoning blank → infer each from the scenario wording → render.

Example 2 — pasted markdown table, bare Input: a | Name | Steps | Expected | table with no tags. → save/pipe to normalizer → it maps the three columns and generates IDs → infer Type/Outcome/Priority/Severity_Reasoning for every row → flag the ones whose intent is ambiguous in the review sidecar → render.

Example 3 — already-tagged CSV Input: a CSV that already has all ten fields (maybe named differently). → normalizer maps and normalizes vocab, reports no gaps → validate → render directly.

Notes

• Never renumber existing IDs; the console and other tooling reference them.
• Color/severity is signal: keep every tag inside the canonical vocab so the console renders it.
• If the source mixes several distinct apps/flows, set Group per row so the facets stay useful.