Principle:Testtimescaling Testtimescaling github io Integration Verification
| Knowledge Sources | Integration testing methodology, CI/CD pipeline verification, GitHub Actions |
|---|---|
| Domains | Quality_Assurance, CI_CD |
| Last Updated | 2026-02-14 |
Overview
End-to-end verification of multi-system integrations ensures that all modified components work together correctly after adding a new paper to the survey.
Description
Integration Verification is the final step in the Adding_a_New_Paper workflow. After modifying multiple connected components -- the comparison table in README.md, the citation registries in papers.json, and the automation script -- this step verifies that all parts function correctly together.
The verification follows a checklist pattern that validates each system boundary in the pipeline:
- Visual verification: The new row appears correctly in the GitHub-rendered README. The paper title is formatted correctly, the arXiv badge links to the correct paper, and all taxonomy values are in the right columns.
- Pipeline trigger: The GitHub Actions citation update workflow can be manually dispatched and completes successfully. This validates that the automation script can parse its inputs and query the Semantic Scholar API.
- Data verification: After the workflow runs, the
arxiv_citations.jsonfile contains an updated total citation count that includes citations for the newly added paper. - Badge verification: The Shields.io citation count badge at the top of the README reflects the updated total, confirming end-to-end data flow from the Semantic Scholar API through the workflow to the visible badge.
Each check validates a different system boundary:
- Visual verification checks the README rendering pipeline (Markdown/HTML to rendered page)
- Pipeline trigger checks the GitHub Actions automation
- Data verification checks the Semantic Scholar API integration
- Badge verification checks the Shields.io external service integration
Usage
Use this principle as the final step after adding a new paper. All changes from Steps 3 (table editing) and 4 (citation registration) must be committed and pushed before verification can begin. This is Step 5 of the Adding_a_New_Paper workflow.
If any verification check fails, the contributor should investigate and fix the issue before considering the paper addition complete. Common failure modes include:
- Malformed HTML in the table row (visual verification fails)
- arXiv ID typo in the Python script (pipeline fails or returns zero citations)
- JSON syntax error in
papers.json(pipeline fails to parse) - Semantic Scholar API rate limiting (transient failure, retry later)
Theoretical Basis
Integration verification draws from established software testing principles adapted for documentation and CI/CD pipeline contexts:
System boundary testing: Each verification check targets a different integration point between systems. This follows the principle that errors are most likely to occur at system boundaries -- where data passes from one system to another (e.g., from JSON file to Python script, from API response to JSON output, from JSON data to Shields.io badge).
Smoke testing: The verification checklist functions as a smoke test -- a quick, high-level validation that the most critical paths through the system are working. It does not exhaustively test every possible failure mode but covers the most common and impactful ones.
End-to-end validation: Rather than testing each component in isolation, the verification triggers the entire pipeline and checks the final output. This approach catches integration issues that component-level testing might miss, such as data format mismatches between the Python script and the JSON output.
Manual trigger pattern: The use of workflow_dispatch as a manual trigger enables on-demand verification without waiting for the scheduled run. This is a standard CI/CD pattern that balances automation with human control -- the workflow is automated in its execution but manually triggered for verification purposes.
Checklist methodology: The verification process uses an explicit checklist rather than relying on memory or intuition. This is based on Atul Gawande's checklist manifesto principle: even experts benefit from systematic checklists for multi-step processes where omitting a step can have cascading consequences.
Fail-fast principle: Each check in the checklist can be evaluated independently. If the first check (visual verification) fails, there is no need to proceed to later checks until the issue is resolved. This fail-fast approach prevents wasted effort on downstream verification when an upstream issue exists.