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arxiv: 2606.04350 · v2 · pith:ZNUIJS3Onew · submitted 2026-06-03 · 💻 cs.SE

Towards Process Mining Use Case Map Models with PM4Py-UCM

Daniel Amyot This is my paper

Pith reviewed 2026-06-30 10:48 UTC · model grok-4.3

classification 💻 cs.SE
keywords process mininguse case mapsURNPM4Pyevent logsrequirements engineeringprocess discoverymodel-driven RE
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The pith

Process mining can output Use Case Map models directly from event logs for use in requirements engineering.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces PM4Py-UCM, an open-source extension to the PM4Py Python library, to discover Use Case Map models from event logs. This makes mined behavior available as input for URN-based modeling, analysis, and management activities instead of limiting outputs to Petri nets or BPMN. A sympathetic reader would care because it connects data from organizational systems to early requirements engineering practices that already rely on UCMs. The work demonstrates a discovery pipeline, hierarchical decomposition into nested models, performer mappings, and round-trip export to jUCMNav that preserves the mined structure.

Core claim

PM4Py-UCM supplies a UCM discovery pipeline, hierarchical decomposition strategies that produce nested UCM models, configurable performer mappings for UCM and BPMN views, and an exporter to jUCMNav that maintains the mined model under round-trip. Illustrations with public and synthetic event logs show the same underlying behavior rendered under varying performer abstractions and decomposition choices, positioning process mining as a practical instrument for model-driven requirements engineering.

What carries the argument

The UCM discovery pipeline that converts event logs into Use Case Map models while supporting hierarchical decomposition and round-trip export to jUCMNav.

If this is right

  • Mined process behavior becomes usable inside URN-based modeling, analysis, and management activities.
  • The same event-log behavior can be shown through different performer abstractions and nested decomposition strategies.
  • Process mining gains a direct role as an instrument for model-driven requirements engineering.
  • Round-trip export allows mined models to move into existing URN tools without loss of structure.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Teams already working with URN standards could incorporate live process data into requirements models with less manual translation.
  • Hierarchical UCMs mined from logs might help requirements engineers manage complexity in large processes by revealing natural nesting levels.
  • Further experiments on real organizational logs could reveal whether the current decomposition strategies scale without manual tuning.

Load-bearing premise

The discovered UCM models faithfully represent the behavior in the event logs and prove practically useful for URN-based requirements engineering work.

What would settle it

A side-by-side check on the same logs where the exported UCM models omit or distort frequent sequences that appear in the original event data or cannot be analyzed correctly inside jUCMNav.

Figures

Figures reproduced from arXiv: 2606.04350 by Daniel Amyot.

Figure 1
Figure 1. Figure 1: The PM4Py-UCM discovery pipeline. • Sequence (→): Activity A must finish before activity B starts. • Exclusive Choice (×): Either activity A or activity B happens, but not both. • Inclusive Choice (∨): Activity A or activity B happens, or both happen. • Parallel (∧) and Interleaving (o): Activities A and B happen concurrently, or in any order. • Loop (⟲): An activity can be repeated multiple times. • Silen… view at source ↗
Figure 2
Figure 2. Figure 2: Models mined from the issue tracking event log. [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Claims payment: UCM view without decomposition (from within the Web interface). [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Claims payment: UCM model exported by PM4Py-UCM to a .jucm file and opened in jUCMNav, with model metrics computed. Note that all 7 maps are there [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Claims payment: UCM view with decomposition. [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
read the original abstract

Given the increasing amount of data available in organizational systems, there is an opportunity for early requirements engineering (RE) activities to be better based on evidence than ever before. Process mining (PM) has been used for over two decades to discover and analyze as-is process models from event logs extracted from such data, with outputs often in the form of Petri Nets, directly-follows graphs, or BPMN models. This paper aims to make Use Case Map (UCM) models, from ITU-T's User Requirements Notation (URN), a first-class output of process discovery, so that mined behavior can be used in URN-based modeling, analysis, and management activities. This paper contributes and illustrates PM4Py-UCM, an open-source extension to the existing PM4Py Python library. This new tool contributes 1) a UCM discovery pipeline, 2) hierarchical decomposition strategies producing nested UCM models, 3) configurable performer mappings for UCM and BPMN visualizations, and 4) an exporter to a URN tool (jUCMNav) that preserves the mined model under round-trip. Using public and synthetic event logs, the paper showcases how the same behavior is rendered under different performer abstractions and decomposition strategies, and discusses how PM can become a practical instrument for model-driven RE.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper claims to extend the PM4Py library with PM4Py-UCM to enable discovery of Use Case Map (UCM) models from event logs, including hierarchical decomposition strategies, configurable performer mappings, and an exporter to jUCMNav that preserves the model for round-trip use. This is positioned to make UCMs a first-class output of process discovery for integration with URN-based requirements engineering, demonstrated through qualitative showcases on public and synthetic event logs.

Significance. If the UCM discovery pipeline produces models that accurately capture log behavior and support requirements engineering tasks, this could significantly bridge process mining and model-driven RE by providing an alternative to Petri nets or BPMN that is directly usable in URN tools. The open-source nature of the extension is a strength, allowing community adoption and further development.

major comments (2)
  1. [Abstract] Abstract: The manuscript states the tool features (UCM discovery pipeline, hierarchical decompositions, performer mappings, jUCMNav exporter) but supplies no implementation details, error handling, testing results, or quantitative evaluation against baselines, leaving the central claim that UCMs become a first-class, practically usable output without demonstrated support.
  2. [Results/showcases] Showcase description (implied evaluation): The qualitative showcases on public/synthetic logs under different abstractions report no conformance metrics (fitness, precision, generalization), no comparison to BPMN/Petri net baselines, and no assessment of whether decompositions preserve behavior or aid RE tasks, which is load-bearing for the claim of faithful representation and practical utility.
minor comments (2)
  1. [Figures] Ensure all figures include clear captions explaining the specific abstraction or decomposition strategy applied.
  2. [Abstract] The contribution list in the abstract could be cross-referenced to specific sections for easier navigation.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our tool paper introducing PM4Py-UCM. We address each major comment below, clarifying the scope of the work as a contribution focused on enabling UCM discovery and integration rather than a full empirical benchmark.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The manuscript states the tool features (UCM discovery pipeline, hierarchical decompositions, performer mappings, jUCMNav exporter) but supplies no implementation details, error handling, testing results, or quantitative evaluation against baselines, leaving the central claim that UCMs become a first-class, practically usable output without demonstrated support.

    Authors: The abstract is written to be concise and highlight the core contributions. Implementation details for the discovery pipeline, hierarchical decomposition strategies, performer mappings, and jUCMNav exporter are provided in Sections 3, 4, and 5 of the manuscript, with the open-source code (including error handling and testing on the reported logs) available for inspection. As the paper positions itself as a tool extension rather than a comparative evaluation study, quantitative baselines were not included; we can revise the abstract to explicitly reference the relevant sections for these details. revision: partial

  2. Referee: [Results/showcases] Showcase description (implied evaluation): The qualitative showcases on public/synthetic logs under different abstractions report no conformance metrics (fitness, precision, generalization), no comparison to BPMN/Petri net baselines, and no assessment of whether decompositions preserve behavior or aid RE tasks, which is load-bearing for the claim of faithful representation and practical utility.

    Authors: We agree that further discussion would strengthen the presentation. Standard conformance metrics are defined for formalisms such as Petri nets and are not directly applicable to UCMs without additional mapping; the discovery algorithm constructs hierarchical decompositions to preserve observed behavior by design. The round-trip export to jUCMNav directly supports integration with URN-based RE tasks. In revision we will add explicit discussion of behavior preservation and how the models can be used for downstream analysis in the showcases section. revision: yes

Circularity Check

0 steps flagged

No circularity; tool extension paper with no derivations or fitted predictions

full rationale

The paper describes a software extension (PM4Py-UCM) for producing UCM models from event logs, including a discovery pipeline, decomposition strategies, mappings, and exporter. It provides qualitative showcases on public/synthetic logs but contains no equations, predictions, fitted parameters, or load-bearing self-citations that reduce to inputs by construction. The contribution is a described implementation and illustration, with no internal derivation chain to inspect for circularity. This matches the default expectation of a non-circular paper.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Tool development paper with no mathematical derivations; contains no free parameters, axioms, or invented entities.

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