aaai-artifact-evaluati…
Use when packaging AAAI code, data, multimedia appendices, technical appendices, reproducibility evidence, and post-acceptance…
Use when writing the related-work and positioning of an ASE (IEEE/ACM Automated Software Engineering) paper, covering the automated-SE literature lanes, delta-first positioning against prior tools/techniques, fair head-to-head framing, and double-anonymous self-citation.
Claude Code
$ npx -y skills add brycewang-stanford/Awesome-Journal-Skills --skill ase-related-work --agent claude-code
Codex
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill ase-related-work -a codex -y
Cursor
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill ase-related-work -a cursor -y
Gemini CLI
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill ase-related-work -a gemini-cli -y
GitHub Copilot
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill ase-related-work -a github-copilot -y
OpenCode
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill ase-related-work -a opencode -y
Windsurf
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill ase-related-work -a windsurf -y
name: ase-related-work description: Use when writing the related-work and positioning of an ASE (IEEE/ACM Automated Software Engineering) paper, covering the automated-SE literature lanes, delta-first positioning against prior tools/techniques, fair head-to-head framing, and double-anonymous self-citation.
Position the paper against **prior automation**, not against a topic. At ASE the question a reviewer holds is "what does this tool/technique do that the existing automated approaches do not?" — so related work is a **delta argument**, and the strongest deltas are stated against named, runnable prior tools you can (and often must) compare against empirically.
An automated-SE paper usually sits at the intersection of several lanes; name your position in each that applies:
interpretation, invariant inference.
flaky-test handling.
repair, patch synthesis.
automation.
automation — and **SE4AI**, applying SE automation to ML systems.
Write each related-work cluster as: *prior automation X does A; it cannot do B because C; we do B.* Then, where B is comparable, **compare against X empirically** — ASE reviewers expect a head-to-head against the closest runnable baseline, not just a prose distinction.
[Cluster] spectrum-based fault localization Prior: ranks statements by suspiciousness from pass/fail spectra. Gap: assumes a fixed test suite; degrades when tests are flaky. Delta: our technique estimates and corrects for flakiness before ranking. Evidence of delta: head-to-head on the same faulty subjects, §5.2, Table 3.
documented budget** (time, iterations, tuning). An under-configured baseline is the classic soundness objection and a Revision criterion waiting to happen.
alternative — do not quietly omit the obvious competitor.
built the baseline will check.
Because automated-SE ideas appear across venues, position precisely: a testing-theory result may live at ISSTA, a broad empirical study at FSE, a language mechanism at a PL venue. When you cite across venues, make the ASE delta — *the automation you add* — explicit, so a reviewer does not read your contribution as a reheated sibling-venue result.
...", or neutrally "The X tool [12] ...") so the citation does not deanonymize you.
is itself a leak.
[Recency] at least the last 2-3 years of ASE/ICSE/FSE/ISSTA automation in your subarea cited? [Closest baseline] is the single most similar runnable tool named AND compared, not just cited? [Cross-venue] PL/ML/testing neighbors positioned so your automation delta is explicit? [Anonymity] every self-citation in the third person; no repository ownership revealed? [Honesty] any obvious competitor conspicuously missing? (a reviewer will notice)
[Lanes] which automated-SE lanes the paper touches [Delta table] prior tool -> what it does -> gap -> your delta -> where the head-to-head lives [Baseline fairness] closest runnable baseline named, version pinned, equal budget? [Anonymity check] self-citations third-person, no repo owner revealed
Stanford REAP × CoPaper.AI · 由斯坦福实证方法论团队精选与维护 访问 copaper.ai 微信:CoPaper.AI 按 11 个主流学科板块覆盖 经管与商科 社会科学 人文学科 数学与物理科学 生命科学 医学与健康 工程与技术 计算机科学与 AI 体育科学 点击任一学科名可跳转到对应说明;每类下的代表子领域在正文总览中完整列出。下方封面墙按 venue 导航,完整分类见覆盖一览。 🧭 布局指南 · 📚 Skill Pack 一览 · ⚡ 如何使用 · 🧪 自动实证
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