Fse Experiments

FSE Experiments

Use this before submission when the empirical story is not yet locked. FSE reviewers are SE empiricists; the evaluation is where a good idea is won or lost. The organizing principle is evidence proportional to the claim — the study must test the thing the paper actually asserts, on subjects and baselines a skeptic would accept.

Evaluation audit

• Match evidence to the claim shape. A claim about developer behavior needs behavior data; a claim about detection needs a labeled ground truth; a claim about scalability needs runtime on realistically sized inputs. Accuracy against a proxy label is not evidence for a practice claim.
• Use real subject systems, sampled by a stated criterion, and list them in the artifact. Toy benchmarks invite the "does this hold on real code?" reject.
• Choose fair baselines, including the strongest prior technique and a simple-but-reasonable alternative, tuned with a documented, equal budget. An untuned baseline is a scored weakness.
• Report SE-standard statistics: effect sizes (not just p-values), confidence intervals, appropriate tests, and corrections for multiple comparisons. Say what variance and how many runs.
• Hold qualitative work to method: coding schemes, inter-rater agreement, saturation, and an audit trail — mixed-methods rigor is native to FSE, not a second-class option.
• Pin provenance for mining and LLM studies (see the code block) so the evaluation reproduces rather than re-samples.
• Design threats in, not on: know before you run which confounds and generalization limits the study will have, and instrument to bound them.

Claim-to-evidence design table

Contamination-aware LLM evaluation

When an LLM is in the loop, the reviewer's first questions are about leakage and reproducibility:

[Contamination]  are test subjects plausibly in the model's training data? report cutoff vs.
                 project dates; prefer post-cutoff or held-out subjects
[Determinism]    fix temperature/seed where possible; report the sampling settings
[Caching]        store raw prompts and raw responses in the artifact; a live-API-only study
                 cannot be reproduced, only re-sampled
[Versioning]     record exact model identifiers and access dates; models change under a name
[Ablation]       isolate the model's marginal value against a non-LLM baseline

Provenance floor for mining studies

• Pin repository SHAs and record the corpus extraction date; archive the extracted dataset, not just the query.
• State inclusion/exclusion criteria and the resulting sample size, with the filtering script in the artifact.
• Report how duplicates, forks, and bot activity were handled — silent inclusion skews every downstream number.

Vignette: evaluating a repair technique

Suppose the paper claims a program-repair technique fixes more real bugs than the prior tool. The matching plan: draw bugs from a real, dated fault dataset; run both tools with an equal, documented time budget; report plausible-and-correct patch counts with confidence intervals and an effect size; manually assess a sample of patches for over-fitting; and state external validity (languages, bug kinds) as a bounded threat — every number traceable to a logged run in the artifact.

Statistical reporting floor

• Effect sizes and confidence intervals for every quantitative comparison; say what the intervals represent.
• Number of runs and the source of variance for any stochastic component.
• The compute actually consumed, not vague feasibility language.

Output format

[Evaluation readiness] strong / adequate / weak
[Claim -> evidence map] <claim: subjects/metric/statistic>
[Baseline fairness] <baseline -> tuned? equal budget? documented?>
[Contamination/provenance] <LLM leakage + mining provenance handled? yes/no>
[Threats-by-design] <confound/generalization -> instrumentation to bound it>
[Decision-critical next run] <one experiment or study extension>

Source: brycewang-stanford/Awesome-Journal-Skills → FSE-Skills/skills/fse-experiments/SKILL.md