icsme-experiments
Use when designing or auditing IEEE ICSME empirical evaluations, covering real evolving subject systems, mining-software-repositories provenance, fair baselines, SE-standard statistics and effect sizes, change-history and survivorship confounds, qualitative rigor, contamination-aware LLM ablations, and matching evidence to the shape of each maintenance/evolution claim.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill icsme-experiments --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 Experiments Use this before submission when the empirical story is not yet locked. ICSME reviewers are maintenance and evolution empiricists, and because the venue has **no revision round**, the evaluation must be complete on submission — you cannot promise a missing analysis and add it later. The organizing principle is **evidence proportional to the claim**, tested on **real systems with real change history**, not on synthetic snapshots. ## Evaluation audit - **Match evidence to the claim shape.** A claim about *maintenance effort* needs effort or proxy data defended as a proxy; a claim about *change impact* needs real change sets; a claim about *comprehension* needs a human study; a claim about *debt* needs a debt measurement, not lines of code. Accuracy against a convenient label is not evidence for a maintenance-practice claim. - **Use real, evolving subject systems,** sampled by a stated criterion over a stated time window, and list them in the artifact. A single snapshot cannot support an evolution claim. - **Pin mining provenance** (see the code block): repository SHAs, extraction dates, inclusion/ exclusion criteria, and fork/duplicate/bot handling. Silent inclusio
- Evaluation audit
- Claim-to-evidence design table
- Provenance floor for mining studies (the ICSME core)
- Contamination-aware LLM evaluation
- Vignette: evaluating a change-impact technique
- Statistical reporting floor
- Output format
What does the icsme-experiments skill do?
Use when designing or auditing IEEE ICSME empirical evaluations, covering real evolving subject systems, mining-software-repositories provenance, fair baselines, SE-standard statistics and effect sizes, change-history and survivorship confounds, qualitative rigor, contamination-aware LLM ablations, and matching evidence to the shape of each maintenance/evolution claim.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill icsme-experiments --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.