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ase-experiments
Use when designing or auditing the evaluation of an ASE (IEEE/ACM Automated Software Engineering) paper, covering real subject systems, fair runnable tool baselines, task-matched effectiveness metrics, ablations that isolate a learned component, oracle and correctness validation, contamination-aware LLM handling, and provenance for mining.
ASE-Skills/skills/ase-experiments/SKILL.md
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# ASE Experiments
Match the evidence to the automation's claim. ASE evaluations are judged on whether a **tool or
technique actually does what it claims on real subjects**, compared **fairly** against the closest
runnable automation. This is the axis reviewers weight most, and the one that most often becomes a
Revision criterion.
## Start from the claim shape
Different automations demand different evidence:
| Automation claim | Evidence that matches | Common failure |
|---|---|---|
| Detection (bugs, smells, vulnerabilities) | Precision/recall/F on real defects with a defined ground truth | Synthetic-only defects; unclear ground truth |