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Abstract notation engineering—replacing recurring low-level construct configurations with higher-level visual patterns—improves perceived clarity and user preference for DCR process models without harming decode accuracy.

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2026-08-03 04:46 UTC pith:DPKTGVET

load-bearing objection Genuinely new method for abstracting recurring visual configurations in modeling languages, honestly evaluated but with an unproven semantic-equivalence gap and only perceptual evidence; worth refereeing, needs revision. the 3 major comments →

arxiv 2607.29552 v1 pith:DPKTGVET submitted 2026-07-31 cs.SE

Improving the Understandability of Conceptual Models via Abstract Notation Engineering

Amine Abbad-Andaloussi , Daniel Jeppe Sch\"utt , Kasper Solh{\o}j J{\o}rgensen , Tobias van Deurs Lundsgaard , Hugo A. L\'opez This is my paper
classification cs.SE
keywords conceptual modelingvisual notationabstract notation engineeringDCR graphsprocess model comprehensionpattern abstractionempirical validationunderstandability
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

The paper's central claim is that conceptual models become easier to read when recurring configurations of low-level constructs are replaced by compact, higher-level visual patterns, and that this can be done systematically rather than case-by-case. It proposes a four-step, language-agnostic method—pattern identification, formalization, visual design, and empirical validation—and applies it to Dynamic Condition Response (DCR) graphs, producing DeCleaR, a set of nine pattern abstractions. In a within-subject experiment with 14 DCR-familiar participants, DeCleaR significantly improved perceived empirical quality, perceived pragmatic quality, and user preference over standard DCR graphs, while answer correctness was statistically indistinguishable. The paper argues this closes part of the 'understandability gap' caused by low-level helper constructs, and that the method transfers to other visual modeling languages.

Core claim

The discovery is that understandability can be improved at the level of recurring configurations of constructs, not just individual symbols. For each of nine common workflow patterns (e.g., Sequence, Deferred Choice, Exists), the authors define the pattern's intended behavior with traces, encode it as a self-contained DCR graph fragment using an encapsulation rule that forbids relations crossing the fragment boundary, and design a visual symbol and label for it through a two-round expert elicitation survey. The empirical study found significant improvements in perceived empirical and pragmatic quality and in user preference for DeCleaR over standard DCR, with no significant difference in the

What carries the argument

The central mechanism is the behavioral encoding: each pattern is translated into a DCR graph fragment with the encapsulation rule that no relation may cross the fragment's boundary, making the abstraction self-contained and substitutable. Paired with this, the four-step method (pattern identification via corpus analysis and expert refinement, formalization via trace-based behavioral specification and encodings, visual design via symbol elicitation, and empirical validation with mixed quantitative-qualitative methods) provides a replicable pipeline. The encapsulation rule is what lets a pattern behave as a unit, so the paper's empirical benefits attach to the abstraction as a unit rather tha

Load-bearing premise

The behavioral encodings of the nine patterns (Section 4.2, Figure 3) correctly capture the intended semantics of each pattern in DCR, despite the lack of a formal equivalence proof.

What would settle it

For any single DeCleaR pattern, find an execution trace that is valid under the pattern's intended behavioral specification (as stated in Table 1 or the text) but is not a valid execution of its DCR encoding in Figure 3, or vice versa; model-checking trace equivalence for each pattern would settle it. Also, if a replication with more participants and all nine patterns showed a significant decline in answer correctness for DeCleaR, the 'no degradation' claim would be contradicted.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

If this is right

  • If DeCleaR's perceived quality benefits hold, tool builders can implement the encodings as transformation rules or visual overlays, preserving existing DCR execution, simulation, and verification support.
  • Other notations (Declare, BPMN, UML, ER) could run the same four-step method to build their own pattern abstractions, since the method is language-agnostic.
  • The perception-performance gap implies that future iterations should address how abstractions interact with surrounding relations, suggesting a concrete design direction for refining DeCleaR.
  • A broader evaluation covering all nine patterns and a larger participant group could reveal whether the accuracy gap remains unchanged, a question the paper explicitly defers to future work.
  • For educators and practitioners, pattern-based abstractions bridge domain vocabulary and formal notation, potentially lowering the expertise barrier to declarative modeling adoption.

Where Pith is reading between the lines

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

  • The method could be applied to static notations: the need for associative entities in ER diagrams or auxiliary nodes in workflow nets is exactly the kind of low-level configuration the method targets, so analogous 'DeCleaR-like' abstractions might be built for those languages.
  • The stable accuracy result hints that perceived quality and decoding accuracy are driven by different cognitive mechanisms; a plausible testable hypothesis is that DeCleaR reduces perceived complexity but not actual cognitive load unless the surrounding relation structure is also simplified.
  • The paper's frequency-based cutoff (top seven patterns with 18+ occurrences, eighth with only 7) offers a quick, principled heuristic for other languages: prioritize patterns with a sharp frequency drop as natural abstraction candidates.
  • A direct extension is an eye-tracking study comparing DeCleaR vs standard DCR to see whether the perception-performance gap reflects altered gaze patterns—for instance, less time searching for pattern meaning, but equal time resolving inter-pattern relations.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

3 major / 4 minor

Summary. The paper proposes a language-agnostic method, called abstract notation engineering, for improving the understandability of conceptual modeling notations. The method consists of four steps: pattern identification, pattern formalization, visual notation design, and empirical validation. The authors instantiate the method on Dynamic Condition Response (DCR) graphs, producing DeCleaR, a set of nine pattern abstractions that replace low-level DCR configurations. They report a counterbalanced within-subject experiment with 14 participants on four selected patterns, claiming significant improvements in perceived empirical quality (H2), pragmatic quality (H3), and user preference (H4) over standard DCR graphs, while answer correctness (H1) showed no significant difference. The paper argues that DeCleaR improves user experience without degrading objective decoding accuracy, and that the method is transferable to other modeling languages.

Significance. If the claims are substantiated, this work makes a valuable contribution to conceptual modeling notation design by shifting the focus from individual symbol improvements to reusable pattern-level abstractions with an explicit encapsulation rule and a systematic evaluation process. The method is grounded in established frameworks (PoN, Semiology of Graphics, SEQUAL) and the empirical study uses a mixed-methods design with counterbalancing, validated questionnaire items, and inter-rater reliability (Cohen's κ=0.82). The authors also provide an online repository with materials and full pattern encodings. These strengths are significant. However, the central claim about DeCleaR's benefits is weakened by two load-bearing gaps: (1) the behavioral encodings in Fig. 3 lack a rigorous semantic equivalence proof, and (2) the empirical evaluation covers only 4 of 9 patterns, making the abstract's general claim exceed the evidence. The paper is a useful proof-of-concept but requires further verification and scope adjustments.

major comments (3)
  1. [§4.2, Fig. 3] The behavioral encodings of the nine patterns are presented as DCR fragments, but the paper does not provide a formal equivalence proof between the trace-based behavioral specifications (Step 2.1) and these DCR fragments. The text states 'We refer the reader to our repository for a detailed explanation of the DCR construct configuration underlying each pattern' but the repository is not a substitute for a rigorous argument in the paper. Without a proof—or at least a model-checked verification for all valid/invalid traces—the empirical results for H2–H4 may be measuring the perceived quality of constructs whose actual DCR semantics differ from the intended patterns. This is load-bearing because the central claim presupposes that each DeCleaR symbol faithfully abstracts the corresponding DCR pattern. Please add a formal equivalence proof or an automated verification step for each pattern,
  2. [§4.4 (Experiment Design)] The empirical study evaluates only four of the nine DeCleaR patterns (Bounded Exists at least N times, Sequence, Explicit Initialization, Explicit Termination), selected through anonymous co-author voting. The abstract and introduction make a general claim: 'DeCleaR improves perceived empirical quality, pragmatic quality, and user preference over standard DCR graphs.' This generalization to all nine patterns is not supported by the data. Either run experiments on the remaining patterns or explicitly restrict the claim to the four tested patterns (and adjust the abstract accordingly). Also, because the pattern selection was not randomized or theory-driven, the observed effects may reflect patterns that are particularly amenable to abstraction.
  3. [§4.4 (Analysis and Cross-Synthesis)] The statistical reporting is incomplete. For the Wilcoxon signed-rank tests (H2, H3, H4), only p-values and median differences are reported. Please include effect sizes (e.g., matched rank-biserial correlation) and confidence intervals for the median differences. With n=14, p-values alone are fragile. Moreover, the H1 non-significance (p=0.23) is incorrectly interpreted in the Abstract as 'without degrading objective decoding accuracy.' A non-significant result does not demonstrate equivalence; it only fails to reject the null. Use an equivalence test (e.g., TOST with a pre-specified bound) or soften the claim to 'no significant difference in answer correctness was detected.'
minor comments (4)
  1. [Fig. 2] Typo in the repeat-loop label: 'theoritical saturation' should be 'theoretical saturation'.
  2. [§2.3] The explanation of the Condition relation 'e→f: f cannot be executed until e has been executed, or e is excluded' is imprecise. In standard DCR semantics, a condition is satisfied if e is executed OR e is excluded (i.e., exclusion makes the condition vacuously true). Clarify this to avoid confusion, especially because the notion of exclusion is used heavily in the encodings.
  3. [Table 1 / §4.2] The Sequence definition 'requiring the entire sequence to complete once initiated' is slightly inconsistent with the valid trace A→B→C→A→B→C, which suggests that each initiation of the first event must eventually be followed by the full sequence. Rephrase to clarify that the pattern applies per occurrence (e.g., 'each occurrence of the first event must be followed by the remaining events in order').
  4. [§4.4] Please report the Likert scale range (e.g., 1–7) and explain how median Δ (e.g., +0.667) is computed, so that the magnitude of the effects is interpretable.

Circularity Check

0 steps flagged

No significant circularity: the empirical claims are measured after design, not derived from fitted inputs.

full rationale

The paper's central claim is empirical: participants perceived DeCleaR patterns as having higher empirical quality, pragmatic quality, and preference compared with standard DCR representations. There is no fitted parameter that is later relabeled as a prediction, and the method's four steps (pattern identification, formalization, visual design, validation) are not defined in terms of the results they produce. The design choices—pattern selection, symbol design, and the tested pattern subset—are informed by author expertise and co-author voting, but the evaluation uses different participants, identical base models, and a counterbalanced within-subject design, so the measured outcomes are not forced by construction. Some cited sources are authored by contributors to this paper (e.g., [42] for the DCR notational variant, [31] for the corpus, [47] for 'behavioral encodings'), but none is load-bearing in a circular way: the notational variant is a presentational detail, the corpus is an external data source, and the behavioral encodings are described and illustrated in the paper itself rather than being merely imported from the citation. The main validity risk is different from circularity: the semantic equivalence between DeCleaR patterns and their DCR encodings is asserted and deferred to the authors' repository ('We refer the reader to our repository for a detailed explanation of the DCR construct configuration underlying each pattern [5]' and the encapsulation rule is stated but not mechanically verified). If an encoding were semantically wrong, the perceptual results would apply to a different construct, but that would be a correctness or evidence-quality issue, not a reduction of the claim to its inputs. The limitation section also acknowledges the narrow scope ('limited participant group and covered few patterns'), which further supports treating the study as an empirical demonstration rather than a closed derivation. Overall, no step in the derivation chain is equivalent by definition to its inputs.

Axiom & Free-Parameter Ledger

1 free parameters · 6 axioms · 1 invented entities

The paper introduces no fitted numerical model, so the only free parameter is the pattern-frequency cutoff that shaped pattern selection. The load-bearing assumptions are the representativeness of the corpus and Delphi panel, the correctness of the DCR behavioral encodings, and the adequacy of the small empirical sample. The new DeCleaR symbols are designed artifacts with in-paper empirical evidence only.

free parameters (1)
  • Pattern-frequency cutoff = 7 occurrences (8th-ranked pattern)
    In Step 1.3, the authors use the sharp drop from 18 occurrences of the 7th pattern to 7 occurrences of the 8th as the selection boundary; this data-driven threshold shapes the DeCleaR pattern set and is not independently justified.
axioms (6)
  • domain assumption The 43 workflow patterns of Russell et al. [38] form an appropriate catalogue of recurring process behaviors.
    Step 1.1 uses this catalogue for compatibility analysis; an incomplete catalogue would miss relevant patterns.
  • domain assumption The 50-text corpus [31] is representative of real declarative process descriptions.
    Step 1.2 derives pattern frequencies from it; a non-representative corpus biases pattern selection.
  • ad hoc to paper The constructed behavioral encodings (Fig. 3) faithfully implement the trace-based behavioral specifications and satisfy the encapsulation rule.
    Section 4.2 asserts this without formal proof; correctness of the encodings is load-bearing for the validation.
  • domain assumption The six-member Delphi panel yields symbol/label designs matching practitioners' mental schemas.
    Step 3.3 relies on this small academic panel; design choices might not generalize.
  • domain assumption Fourteen DCR-familiar participants and four patterns suffice to test the hypotheses.
    Section 4.4 has no power analysis; small N limits statistical conclusiveness.
  • domain assumption The adapted SEQUAL questionnaire items [35] validly measure perceived empirical and pragmatic quality.
    Section 4.4 uses them as dependent variables; if invalid, H2/H3 results are not interpretable.
invented entities (1)
  • DeCleaR pattern abstractions (Sequence, Deferred Choice, Bounded Exists, Explicit Initialization, Explicit Termination, Exists, isAbsent, Precedes, LeadsTo) no independent evidence
    purpose: Replace recurring low-level DCR configurations with compact, semantically transparent visual constructs.
    New notational constructs introduced by the paper; only four of nine were empirically assessed inside the paper and no independent external validation or formal semantics proof is provided.

pith-pipeline@v1.3.0-daily-deepseek · 11601 in / 12245 out tokens · 112470 ms · 2026-08-03T04:46:02.745580+00:00 · methodology

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read the original abstract

Conceptual modeling supports the design, analysis, and communication of the properties of complex systems, yet conceptual models can be difficult to understand when domain-level abstractions must be encoded through low-level constructs required mainly for semantic conformity. Prior work has mainly improved how existing individual constructs are visually represented. We shift the focus from individual constructs to recurring configurations of constructs, and propose abstract notation engineering as a language-agnostic method for replacing such configurations with higher-level, semantically transparent constructs. The method comprises pattern identification, pattern formalization, visual notation design, and empirical validation. We instantiate it for Dynamic Condition Response (DCR) graphs, where common workflow patterns require elaborate low-level configurations. The resulting extension, DeCleaR, replaces such configurations with compact pattern-based abstractions. The results of our empirical validation show that DeCleaR improves perceived empirical quality, pragmatic quality, and user preference over standard DCR graphs.

Figures

Figures reproduced from arXiv: 2607.29552 by Amine Abbad-Andaloussi, Daniel Jeppe Sch\"utt, Hugo A. L\'opez, Kasper Solh{\o}j J{\o}rgensen, Tobias van Deurs Lundsgaard.

Figure 1
Figure 1. Figure 1: Simplified passenger airport journey modeled in DCR Graphs. [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the notation engineering method. [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: DCR model fragments of the identified patterns. For the sake of illustration, we set N to 3. [PITH_FULL_IMAGE:figures/full_fig_p011_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: DeCleaR Patterns. Note: The Bounded Exists patterns (e,f,g) include a dynamic circular bar [PITH_FULL_IMAGE:figures/full_fig_p012_4.png] view at source ↗

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Reference graph

Works this paper leans on

48 extracted references · 1 linked inside Pith

  1. [1]

    SoSyM (2020)

    Abbad Andaloussi, A., Zerbato, F., Burratin, A., Slaats, T., Hildebrandt, T., We- ber, B.: Exploring how users engage with hybrid process artifacts based on declar- ative process models: a behavioral analysis based on eye-tracking and think-aloud. SoSyM (2020)

    2020

  2. [2]

    Abbad-Andaloussi, A., Buch-Lorentsen, J., L´ opez, H.A., Slaats, T., Weber, B.: Exploring the modeling of declarative processes using a hybrid approach. In: ER. pp. 162–170. Springer (2019)

    2019

  3. [3]

    Expert Systems with Applications233, 120924 (2023) Understandability of Conceptual Models via Abstract Notation Engineering 15

    Abbad-Andaloussi, A., Burattin, A., Slaats, T., Kindler, E., Weber, B.: Complexity in declarative process models: Metrics and multi-modal assessment of cognitive load. Expert Systems with Applications233, 120924 (2023) Understandability of Conceptual Models via Abstract Notation Engineering 15

    2023

  4. [4]

    Pro- cess Science2(1), 19 (2025)

    Abbad-Andaloussi, A., Franceschetti, M., L´ opez, H.A., Schreiber, C., Weber, B.: When a model gives you mixed signals: cognitive effects and visual behavior. Pro- cess Science2(1), 19 (2025)

    2025

  5. [5]

    Abbad-Andaloussi, A., Sch¨ utt, D.J., Kasper Solhøj Jørgensen, T.v.D.L., L´ opez, H.A.: Improving the understandability of conceptual models via abstract notation engineering (online repository),https://github.com/aminobest/DeCleaR

  6. [6]

    Information Systems91, 101505 (2020)

    Andaloussi, A.A., Burattin, A., Slaats, T., Kindler, E., Weber, B.: On the declara- tive paradigm in hybrid business process representations: A conceptual framework and a systematic literature study. Information Systems91, 101505 (2020)

    2020

  7. [7]

    University of Wisconsin press (1983)

    Bertin, J.: Semiology of graphics. University of Wisconsin press (1983)

    1983

  8. [8]

    Information Systems p

    Blasilli, G., Ferro, L.S., Lenti, S., Maggi, F.M., Marrella, A., Catarci, T.: Improving the understandability of declarative process discovery results using easydeclare. Information Systems p. 102667 (2025)

    2025

  9. [9]

    In: ICPM

    Caballero-Villalobos, J., Jensen, S., L´ opez, H.A.: 3DCR: A tool for immersive process mining. In: ICPM. CEUR-WS (2024)

    2024

  10. [10]

    In: EDOC Workshop

    Carvalho, R., Boubaker, A., Gonzalez-Huerta, J., Mili, H., Ringuette, S., Charif, Y.: On the analysis of cmmn expressiveness: revisiting workflow patterns. In: EDOC Workshop. pp. 1–8 (2016)

    2016

  11. [11]

    Data & Knowledge Engineering157, 102413 (2025)

    Christfort, A.K., Cosma, V.P., Debois, S., Hildebrandt, T.T., Slaats, T.: Static and dynamic techniques for iterative test-driven modelling of dynamic condition response graphs. Data & Knowledge Engineering157, 102413 (2025)

    2025

  12. [12]

    Debois, S., Hildebrandt, T., Slaats, T.: Hierarchical declarative modelling with refinement and sub-processes. In: BPM. pp. 18–33. Springer (2014)

    2014

  13. [13]

    Debois, S., L´ opez, H.A., Slaats, T., Andaloussi, A.A., Hildebrandt, T.T.: Chain of events: modular process models for the law. In: iFM. pp. 368–386. Springer (2020)

    2020

  14. [14]

    Dumas, M., La Rosa, M., Mendling, J., Reijers, H.A., et al.: Fundamentals of business process management, vol. 1. Springer (2013)

    2013

  15. [15]

    In: Proceedings of the 21st international conference on Software engineering

    Dwyer, M.B., Avrunin, G.S., Corbett, J.C.: Patterns in property specifications for finite-state verification. In: Proceedings of the 21st international conference on Software engineering. pp. 411–420 (1999)

    1999

  16. [16]

    SoSyM15(1), 119–146 (2016)

    Elgammal, A., Turetken, O., van den Heuvel, W.J., Papazoglou, M.: Formalizing and appling compliance patterns for business process compliance. SoSyM15(1), 119–146 (2016)

    2016

  17. [17]

    In: International Workshop on Business Process Modeling, De- velopment and Support

    Fahland, D., L¨ ubke, D., Mendling, J., Reijers, H., Weber, B., Weidlich, M., Zu- gal, S.: Declarative versus imperative process modeling languages: The issue of understandability. In: International Workshop on Business Process Modeling, De- velopment and Support. pp. 353–366. Springer (2009)

    2009

  18. [18]

    BISE59(1), 41–67 (2017)

    Figl, K.: Comprehension of procedural visual business process models: a literature review. BISE59(1), 41–67 (2017)

    2017

  19. [19]

    Figl, K., Di Ciccio, C., Reijers, H.A.: Do declarative process models help to reduce cognitive biases related to business rules? In: ER 2020. pp. 119–133. Springer (2020)

    2020

  20. [20]

    Genon, N., Heymans, P., Amyot, D.: Analysing the cognitive effectiveness of the bpmn 2.0 visual notation. In: SLE. pp. 377–396. Springer (2010)

    2010

  21. [21]

    In: International IFIP Working Conference on Enterprise Interoperability

    Han, Z., Zhang, L., Ling, J., Huang, S.: Control-flow pattern based transformation from uml activity diagram to yawl. In: International IFIP Working Conference on Enterprise Interoperability. pp. 129–145. Springer (2012)

    2012

  22. [22]

    Softwaretechnik-Trends36(2), 53–56 (2016)

    Hanser, M., Di Ciccio, C., Mendling, J., et al.: A new notational framework for declarative process modeling. Softwaretechnik-Trends36(2), 53–56 (2016)

    2016

  23. [23]

    In: FSEN

    Hildebrandt, T., Mukkamala, R.R., Slaats, T.: Nested dynamic condition response graphs. In: FSEN. pp. 343–350. Springer (2011) 16 Abbad-Andaloussi et al

    2011

  24. [24]

    In: ICSSP

    Hildebrandt, T.T., Andaloussi, A.A., Christensen, L.R., Debois, S., Healy, N.P., L´ opez, H.A., Marquard, M., Møller, N.L., Petersen, A.C., Slaats, T., et al.: Eco- know: Engineering effective, co-created and compliant adaptive case management systems for knowledge workers. In: ICSSP. pp. 155–164 (2020)

    2020

  25. [25]

    arXiv preprint arXiv:1110.4161 (2011)

    Hildebrandt, T.T., Mukkamala, R.R.: Declarative event-based workflow as dis- tributed dynamic condition response graphs. arXiv preprint arXiv:1110.4161 (2011)

    Pith/arXiv arXiv 2011

  26. [26]

    OUP Oxford (2011)

    Holmqvist, K., Nystr¨ om, M., Andersson, R., Dewhurst, R., Jarodzka, H., van de Weijer, J.: Eye Tracking: A comprehensive guide to methods and measures. OUP Oxford (2011)

    2011

  27. [27]

    In: BPM Forum

    Jensen, S.J., L´ opez, H.A.: Towards immersive environments for declarative process models. In: BPM Forum. pp. 214–231. Springer (2024)

    2024

  28. [28]

    Springer (2016)

    Karagiannis, D., Mayr, H.C., Mylopoulos, J.: Domain-specific conceptual modeling. Springer (2016)

    2016

  29. [29]

    In: HICSS

    Keramidis, P., Hildebrandt, T.T.: On the business value of declarative business process management. In: HICSS. pp. 5631–5640 (2026)

    2026

  30. [30]

    Springer Science & Business Media (2012)

    Krogstie, J.: Model-based development and evolution of information systems: A Quality Approach. Springer Science & Business Media (2012)

    2012

  31. [31]

    L´ opez, H.A., Feng, B., Lindner, J., Franceschetti, M., Abbad-Andaloussi, A.: Am- biguity detection in business process descriptions: An evidence and an automated approach. In: BPM. pp. 379–396. Springer (2025)

    2025

  32. [32]

    In: POEM

    L´ opez, H.A., Simon, V.D.: How to (re) design declarative process notations? a view from the lens of cognitive effectiveness frameworks. In: POEM. CEUR-WS (2022)

    2022

  33. [33]

    IEEE Trans

    Moody, D.: The “physics” of notations: toward a scientific basis for constructing visual notations in software engineering. IEEE Trans. Softw. Eng.35(6), 756–779 (2009)

    2009

  34. [34]

    Information & management42(1) (2004)

    Okoli, C., Pawlowski, S.D.: The delphi method as a research tool: an example, design considerations and applications. Information & management42(1) (2004)

    2004

  35. [35]

    Ieee Access13, 140742–140770 (2025)

    Pacheco, D., Aveiro, D., Pinto, D., Freitas, V.: User-perceived quality and func- tional suitability of representations of demo’s process and fact models: Conclusions from a family of three studies. Ieee Access13, 140742–140770 (2025)

    2025

  36. [36]

    In: EDOC 2007

    Pesic, M., Schonenberg, H., Van der Aalst, W.M.: Declare: Full support for loosely- structured processes. In: EDOC 2007. pp. 287–287. IEEE (2007)

    2007

  37. [37]

    Reijers, H.A., Slaats, T., Stahl, C.: Declarative modeling–an academic dream or the future for bpm? In: BPM. pp. 307–322. Springer (2013)

    2013

  38. [38]

    Russell, N.C., Ter Hofstede, A.H., Van Der Aalst, W.M., Mulyar, N.A.: Workflow control-flow patterns: A revised view (2006)

    2006

  39. [39]

    SAGE Publications, 4th edn

    Salda˜ na, J.: The Coding Manual for Qualitative Researchers. SAGE Publications, 4th edn. (2021)

    2021

  40. [40]

    In: FSEN

    Siegmund, J., Peitek, N., Parnin, C., Apel, S., Hofmeister, J., K¨ astner, C., Begel, A., Bethmann, A., Brechmann, A.: Measuring neural efficiency of program com- prehension. In: FSEN. pp. 140–150 (2017)

    2017

  41. [41]

    Scholarpedia3(12), 5345 (2008)

    Todorovic, D.: Gestalt principles. Scholarpedia3(12), 5345 (2008)

    2008

  42. [42]

    In: CoopIS

    Trinh, D.M.T., Abbad-Andaloussi, A., L´ opez, H.A.: On the semantic transparency of declarative process models: the case of constraints. In: CoopIS. pp. 217–236. Springer (2023)

    2023

  43. [43]

    Turetken, O., Rompen, T., Vanderfeesten, I., Dikici, A., van Moll, J.: The effect of modularity representation and presentation medium on the understandability of business process models in bpmn. In: BPM. pp. 289–307. Springer (2016) Understandability of Conceptual Models via Abstract Notation Engineering 17

    2016

  44. [44]

    IEEE Transactions on Cognitive and Developmental Systems14(1), 164–180 (2022)

    Winter, M., Pryss, R., Probst, T., Baß, J., Reichert, M.: Measuring the cognitive complexity in the comprehension of modular process models. IEEE Transactions on Cognitive and Developmental Systems14(1), 164–180 (2022)

    2022

  45. [45]

    Wohlin, C., Runeson, P., H¨ ost, M., Ohlsson, M.C., Regnell, B., Wessl´ en, A., et al.: Experimentation in software engineering. vol. 236. Springer (2012)

    2012

  46. [46]

    In: 10th IEEE international conference on collaborative computing: networking, applications and worksharing

    Zeising, M., Sch¨ onig, S., Jablonski, S.: Towards a common platform for the support of routine and agile business processes. In: 10th IEEE international conference on collaborative computing: networking, applications and worksharing. IEEE (2014)

    2014

  47. [47]

    In: FormaliSE ’26

    Zhou, Y., L´ opez, H.A.: A compositional approach to mapping bpmn to dcr for cross-paradigm process modeling. In: FormaliSE ’26. vol. To appear (2026)

    2026

  48. [48]

    Zugal, S.: Applying Cognitive Psychology for Improving the Creation, Understand- ing and Maintenance of Business Process Models. Ph.D. thesis, University of Inns- bruck (2013)

    2013