icsme-reproducibility
Use when strengthening IEEE ICSME reproducibility and open-science evidence, covering the data-availability statement, anonymized-but-runnable artifacts, mining and LLM provenance pinning, claim-to-evidence mapping, honest degrees of reproducibility, and consistency between the paper and the artifact ahead of the ROSE-Festival IEEE badges.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill icsme-reproducibility --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# ICSME Reproducibility Use this before submission and again before camera-ready. ICSME's ROSE-Festival culture makes reproducibility a valued, sometimes scored dimension rather than a courtesy: double-anonymous review already expects an inspectable artifact, and the **Joint Artifact Evaluation Track and ROSE Festival** rewards a permanent, open one with IEEE badges. The goal is that a competent maintainer could rebuild your evidence — re-mine the corpus, re-run the technique — and reach your conclusions. ## Evidence map - Map each research-question answer, technique claim, and reported number to a **verifiable location** — a paper section, a table generated from logged data, or a script in the artifact. - For techniques, give enough of the algorithm, parameters, and environment that a reader could re-implement or re-run it on their own systems. - For mining/evolution studies, report subject systems and their selection, the extraction pipeline, preprocessing, metrics, statistics, and the analysis scripts. - Keep the **data-availability statement** truthful and specific: what is shared (corpus, scripts, subject list), where it will live after acceptance, and — if something cannot be
- Evidence map
- Data-availability statement audit
- Provenance pinning
- Degrees of reproducibility (state the one you achieved)
- Vignette: a mixed-methods evolution study
- Consistency and camera-ready / ROSE pass
- Output format
What does the icsme-reproducibility skill do?
Use when strengthening IEEE ICSME reproducibility and open-science evidence, covering the data-availability statement, anonymized-but-runnable artifacts, mining and LLM provenance pinning, claim-to-evidence mapping, honest degrees of reproducibility, and consistency between the paper and the artifact ahead of the ROSE-Festival IEEE badges.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill icsme-reproducibility --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From brycewang-stanford/Awesome-Journal-Skills, a repository with 909 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.