Skip to content

STATUS.md

Latest commit

 

History

History
77 lines (59 loc) · 3.32 KB

STATUS.md

File metadata and controls

77 lines (59 loc) · 3.32 KB
Raw

STATUS — Artifact Badge Claims

We apply for the following three ACM badges under the "Artifact Review and Badging Version 1.1" policy:

  1. Artifact Evaluated — Functional
  2. Artifact Evaluated — Reusable
  3. Artifact Available

Artifact Evaluated — Functional

The README (README.md) clearly describes the artifact's contents, purpose, and how to obtain it. It is extended with:

  • INSTALL.md : full installation guide covering hardware/software prerequisites (Modelio 5.4, Python 3.10+, OpenRouter API key), OS-specific setup steps for Windows, Linux, and macOS, and placement of the helper library inside Modelio's macro directory.
  • docs/QUICKSTART.md : a small self-contained example that walks through generating a BPMN model for a single scenario end-to-end that works within minutes of installation.
  • REPRODUCE.md : step-by-step guide to reproduce every result in the paper: download the PMo dataset, run the generation pipeline with tools/run_pipeline.py, test a generated macro in Modelio, and rerun Evaluation/results/Evals.ipynb to regenerate Tables 1–5.
  • requirements.txt is provided as all Python dependencies can be installed with a single pip install -r requirements.txt.

Artifact Evaluated — Reusable

We believe this artifact significantly exceeds minimal functionality and is structured for reuse.

Well-structured layout and packaging. The repository follows a consistent layout separating concerns where every folder carries its own README.md: prompt approaches (approaches/), evaluation data (Evaluation/), and utility scripts (tools/). Dependencies are declared in requirements.txt, and INSTALL.md covers Modelio setup and the helper library placement for Windows, Linux, and macOS.

Reusable prompt approaches. Both approaches (config-helpers and no-helper) are self-contained: each has a system_prompt.md, documented examples, and a README.md. A researcher can drop in any other LLM supported by OpenRouter by changing only the --model flag in run_pipeline.py.

Reusable helper library. BPMN_Helpers.py is documented at the API level (approaches/config-helpers/docs/) and designed to absorb Modelio API surface details so that LLM-facing prompts remain model-agnostic. It can be reused for any BPMN automation task in Modelio beyond the scenarios in this paper.

Open, reusable data formats. All experiment data is stored as JSONL (one object per scenario/model/approach), with a stable field schema documented in Evaluation/dataset/README.md. The PMo benchmark dataset is independently archived on Zenodo (doi:10.5281/zenodo.15857589).

Extensibility. tools/bpmn_to_complexity.py can compute complexity metrics for any BPMN XML file, independent of this paper's scenarios. tools/run_pipeline.py accepts arbitrary --input JSONL files, enabling researchers to run the same pipeline on new scenario sets without modifying any code.

Artifact Available

We believe this artifact satisfies the Available criteria on the following grounds.

Publicly accessible. The artifact is published on Zenodo at a stable, persistent URL. DOI: 10.5281/zenodo.20931887

Open license. The software components of this artifact are released under the Apache License 2.0 (see LICENSE).