Tacas experiments

TACAS Experiments

Use this before submission when the empirical story is not yet locked. TACAS reviewers are verification experts, and the evaluation is where a tool or algorithm is won or lost. The organizing principle is honest, reproducible comparison: the experiment must test the claim on shared benchmarks, against a fairly configured baseline, with every number reproducible in the artifact (mandatory for tool papers).

Evaluation audit

• Use community benchmarks. Draw tasks from established suites (e.g., SV-COMP task sets, model- checking or SMT benchmark libraries, prior tool distributions) rather than a private set of favourable inputs. A benchmark nobody else uses invites the "cherry-picked" reject.
• Configure baselines fairly. Compare against the strongest available competing tool, with a documented, equal time and memory budget on the same hardware. An untuned or crippled baseline is a scored weakness, and reviewers often know the baseline's authors.
• Report the right quantities. Solved/unsolved counts, wall-clock time with the timeout stated, memory, and the largest instance handled — not a single ratio. State the machine (CPU, RAM) and the number of repetitions for any variance.
• Check your results for soundness. Verification tools can be fast because they are wrong: report how you validated answers (cross-checking against a reference tool, witness validation, known expected verdicts), and disclose any incorrect results rather than hiding them.
• Reproduce in the artifact. Every table and figure must regenerate from a script in the artifact on the clean ETAPS VM; a tool paper whose numbers cannot be reproduced fails the mandatory artifact evaluation and endangers the paper.
• Bound external validity. Say which languages, property classes, or system sizes the results cover, and name the ones they do not.

Claim-to-evidence design table

Verification claim	Matching evidence	Reject pattern avoided
"Verifies more tasks than prior tools"	Solved counts on a shared benchmark set vs a tuned baseline, equal timeout	"Evaluated on our own examples only"
"Faster / more scalable"	Wall-clock and memory across realistic sizes, hardware stated	"Speedup ratio with no timeout or machine given"
"Finds real bugs"	Reproducible counterexamples/witnesses on real code, validated	"Warnings with no confirmed true positives"
"Sound (or sound up to k)"	Correctness argument + no incorrect verdicts on a validation set	"Fast because it silently under-approximates"
"General technique"	Multiple property classes / languages + stated limits	"One benchmark family, claimed universal"

Fair-comparison checklist

[Baseline]    strongest competitor, latest version, cited; not a straw man
[Budget]      identical timeout and memory limit for every tool; state them
[Hardware]    one machine, described; note any parallelism and core counts
[Tasks]       a named, shared benchmark set; report per-category, not just totals
[Validation]  answers cross-checked / witnesses validated; incorrect results disclosed
[Determinism] fix seeds/options; report variance across repetitions where relevant

SV-COMP vs a TACAS tool-paper evaluation

TACAS hosts SV-COMP, but a competition entry and a tool-paper evaluation are different deliverables — do not conflate them:

• SV-COMP runs your verifier on the common task set under the organizers' harness and rules, and reports a ranked, uniform comparison across all participants; your contribution is a short competition paper plus a conforming tool.
• A tool paper is peer-reviewed prose making a specific claim about your tool, evaluated on benchmarks you justify, judged on contribution and a reproducible artifact — not on a leaderboard position. You may use SV-COMP benchmarks in a tool paper, but cite them and keep the comparison fair and reproducible.

Vignette: evaluating a new model checker

Suppose the paper claims a new checker verifies more C tasks than the prior tool. The matching plan: take a shared C benchmark set (with categories), run both tools with an identical timeout and memory limit on one stated machine, report per-category solved/unsolved and wall-clock, validate verdicts (cross-check disagreements, validate violation witnesses), disclose any wrong answers, state which property classes are out of scope, and ship a clean-VM artifact whose scripts regenerate every table.

Reporting floor

• Machine description, timeout, and memory limit for every experiment.
• Per-benchmark or per-category results, not only aggregate totals.
• A soundness/validation statement and honest disclosure of incorrect results.
• Artifact scripts that regenerate each table/figure on the ETAPS VM.

Output format

[Evaluation readiness] strong / adequate / weak
[Claim -> evidence map] <claim: benchmark set / metric / baseline>
[Baseline fairness] <baseline -> latest? equal budget? same hardware? documented?>
[Soundness] <validation method; any incorrect results disclosed? yes/no>
[Reproducibility] <every number regenerates on the clean VM? yes/no>
[Decision-critical next run] <one experiment or validation to add>