ISSTA Artifact Evaluation

Use this to package evidence for ISSTA's artifact-evaluation track. ISSTA has a genuinely strong artifact culture: a runnable tool and a shared benchmark are treated as normal, and the badges carry weight. Reopen the current artifact call before packaging — the badge set and the archival requirement are cycle-specific.

The ACM badge targets

Available is about archival; Functional and Reusable are about engineering quality; Results Reproduced is about matching the paper. They are earned independently — decide which you are going for before you package.

Artifact plan

• Decide the badge set, then package to its bar. A Reusable badge needs documentation and structure a Functional-only artifact can skip.
• Ship the tool with a single documented entry point and a container or environment file, so an evaluator reaches a result without reconstructing your machine.
• Pin subjects and benchmark versions: the exact Defects4J revision, subject-program commit SHAs, and any fuzzing seed corpora, archived rather than referenced by name.
• Provide a results-regeneration path: a script that takes logged runs to the paper's tables, so "Results Reproduced" is a command, not an argument.
• Anonymize the review-time copy — repository owners, commit authors, container labels — because ISSTA artifact review is double-anonymous alongside the paper.
• After acceptance, make the Zenodo deposit public and citable, and record its DOI for the camera-ready badge display.

What ISSTA evaluators try first

• The README's quick-start, then the single command that produces one headline number. If that fails on a clean machine, no amount of internal quality is visible.
• A small, fast subset that finishes in minutes, before any full multi-day fuzzing or symbolic-execution campaign. Ship a "smoke" configuration explicitly.
• The mapping from a paper claim to the artifact output that supports it; an artifact whose outputs cannot be tied back to Table N reads as unverifiable.

Handling long-running and non-deterministic tools

Testing and analysis artifacts often run for hours and vary between runs. Package for that reality:

artifact/
  README.md            # quick-start, smoke config, full config, expected outputs, runtime
  Dockerfile           # pinned toolchain and dependencies
  subjects/            # pinned subject SHAs or a fetch script that pins them
  run_smoke.sh         # minutes: reproduces one representative row
  run_full.sh          # hours/days: reproduces all tables
  results/             # logged raw outputs from the authors' runs
  scripts/tables.py    # regenerates paper tables from results/

State the run count and expected variance for non-deterministic results, and have the regeneration script accept the evaluator's fresh runs as well as the shipped logs, so a partial reproduction still lands on the paper's numbers within tolerance.

Calibration anchors

• Evaluators are time-boxed; assume they run the smoke config and skim the full one. Design so the smoke path alone justifies Functional.
• Badge names, the Zenodo requirement, and any single-blind vs. double-blind detail vary by cycle; verify against the current artifact call rather than a past year.

Output format

[Badge target] Available / Functional / Reusable / Results Reproduced
[Entry point] <command + smoke runtime>
[Pinned subjects] <benchmark version / SHAs archived?>
[Reproduction level] turnkey / scripted / descriptive / weak
[Anonymity risks] <owners/authors/labels/paths>
[Fixes before upload] <ordered list>