The most direct way to review whether a paper's code is actually reusable is to check three things: whether the code is publicly accessible, whether it runs without undocumented modifications, and whether its dependencies and data are clearly specified. Start with a quick accessibility audit, then move to a hands-on reproducibility test.
Step 1: Check the Paper for a Code Availability Statement
Many journals and conferences now require a "Data and Code Availability" section. Look for this near the end of the paper. If it mentions a repository (GitHub, Zenodo, Figshare, or a project site), note the exact URL and version. If no statement exists, search for the paper title plus "code" on GitHub or Google Scholar — but be cautious: unofficial mirrors may be outdated or incomplete.
Step 2: Inspect the Repository Structure
Once you find the code, open it and look for:
- A README file that explains installation, usage, and expected outputs.
- A license (MIT, Apache, GPL, etc.) — no license means you legally cannot reuse it.
- Dependency files (requirements.txt, environment.yml, package.json) — these should list versions, not just package names.
- Example scripts or test data that let you run the code immediately without sourcing external datasets.
If the README is vague or missing, treat that as a red flag for reusability, even if the code itself is well-written.
Step 3: Run the Code in a Clean Environment
The real test is execution. Create a fresh virtual environment or container (e.g., Docker) and follow the README step-by-step. Pay attention to:
- Installation errors — are missing packages named clearly?
- Hard-coded paths — does the code assume a specific directory structure that you must manually recreate?
- Input data format — are the expected file formats and schemas documented?
- Runtime errors — do they give meaningful error messages or fail silently?
If you can reproduce the paper’s key figure or metric with the provided example data, the code is likely reusable. If you hit a wall, check the repository’s issue tracker — active maintainers often answer questions, which is itself a good sign.
Step 4: Assess Portability and Maintenance
Reusability also means the code will work in your context, not just the author’s. Ask:
- Is the code modular (functions/classes) or a single monolithic script?
- Does it depend on proprietary tools or paid datasets?
- When was the last commit? Code untouched for 3+ years may break with modern library versions.
A Practical Shortcut
If you’re reviewing many papers, you don’t have time to fully run each one. WisPaper’s Scholar QA can help you quickly extract the code-related claims from the paper’s methods section and cross-check them against the actual repository contents — so you can spot mismatches (e.g., the paper says “Python 3.8” but the code uses 3.12 syntax) before you invest hours in setup.
Final Checklist
For a quick verdict, score the paper on:
- Accessibility — Is the code public and linked?
- Documentation — Is the README complete and accurate?
- Reproducibility — Did it run on your machine without hacks?
- Portability — Can you adapt it to your data and environment?
If you answered “yes” to all four, the code is genuinely reusable. If you answered “no” to any, you’ll likely spend more time fixing the code than writing your own — and that’s a valid reason to move on.
