=Paper=
{{Paper
|id=Vol-2575/paper5
|storemode=property
|title=BPMN in the Wild: BPMN on GitHub.com
|pdfUrl=https://ceur-ws.org/Vol-2575/paper5.pdf
|volume=Vol-2575
|authors=Thomas Heinze,Viktor Stefanko,Wolfram Amme
|dblpUrl=https://dblp.org/rec/conf/zeus/HeinzeSA20
}}
==BPMN in the Wild: BPMN on GitHub.com==
https://ceur-ws.org/Vol-2575/paper5.pdf
BPMN in the Wild: BPMN on GitHub.com
Thomas S. Heinze1 , Viktor Stefanko2 , and Wolfram Amme2
1
German Aerospace Center (DLR)
thomas.heinze@dlr.de
2
Friedrich Schiller University Jena
[wolfram.amme,viktor.stefanko]@uni-jena.de
Abstract. We present our efforts in creating and analyzing a corpus of
BPMN process models by mining software repositories. Systematically
searching for BPMN process artifacts in 6,163,217 repositories or 10%
of all repositories hosted on GitHub.com, at the time of conducting our
research, resulted in a diverse corpus of 8,904 BPMN 2.0 process models.
1 Introduction
Within the last years, an increasing number of software projects have shifted
towards using platforms such as GitHub.com for their software development. Using
these platforms as a source of data for empirical research allows for addressing a
wide range of questions on the practice of software development and receives more
and more attention, as indicated by the popularity of the flagship conference on
the topic: International Conference on Mining Software Repositories (MSR)1 .
Research in the domain of business process modeling can as well benefit from
such a data-driven approach. Due to characteristics of the domain, i.e., “process
equals product”, there is a lack of larger and commonly available datasets with
real-world process models, which hinders empirical research in this area [2,11,13].
Mining software repositories, i.e., systematically retrieving, processing and an-
alyzing process models from software repositories hosted on platforms such
as GitHub.com, can help to overcome this lack and provides a complimentary
approach to empirical research besides existing methods like case studies, experi-
ments, and surveys. For example, research questions on how a language such as
the Business Process Model and Notation (BPMN) [1] is used in practice can be
addressed, in order to differentiate between the frequently and the rarely used
parts of the language, thus advancing language and tool development. Analyzing
modeling styles furthermore allows for investigating best practices and guidelines
to help process designers. Eventually, best practices and tools as proposed by
academic research or industry can be evaluated more realistically [12].
In this paper, we present our approach for mining software repositories on
GitHub.com to create and analyze a corpus of BPMN process models. Due to
the sheer number of repositories on GitHub.com and time constraints, we limited
our approach to a randomly selected subset of 6,163,217 repositories or 10% of
1
http://www.msrconf.org
J. Manner, S. Haarmann, S. Kolb, O. Kopp (Eds.): 12th ZEUS Workshop, ZEUS 2020, Potsdam,
Germany, 20-21 February 2020, published at http://ceur-ws.org/Vol-2575
Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License
Attribution 4.0 International (CC BY 4.0).
BPMN in the Wild 27
GitHub
GHTorrent GitHub
API v3
1 Repository 2 Data 3 Filtering / 4
Analysis
Selection Extraction Cleansing
Fig. 1. Schematic illustration of the mining pipeline.
all software repositories on GitHub.com at the time of conducting the research.
As a result, we were able to identify and analyze 8,904 distinct process models
which are defined using BPMN 2.0’s XML-based serialization format.
2 Related Work
The Lindholmen dataset has been an inspiration for this paper [5,12]. In Hebig et
al. [5], the authors describe their approach to mine GitHub.com for UML models
and report on gained insights. The dataset is considerably larger than our corpus,
counting 93,596 models [12]. UML though is a family of general-purpose modeling
languages while BPMN is one domain-specific modeling language. We are not
aware of other work, which mines software repositories for BPMN models.
There have also been community efforts to create model collections [9]. The
BPM Academic Initiative provides a platform to create and share business
process models for academic teaching [11]. According to Ho-Quang et al. [9],
the recent number of models is 29,285, but data collection has discontinued and
the focus is on conceptual models as most models originate from students. A
similar platform has been introduced last year under the name RePROSitory [2],
including 174 business process models in its current database. Another initiative
is the BenchFlow project, where business process models were collected from
industrial partners. The authors claim to have collected 8,363 models, with a
share of 64% of BPMN [13]. Unfortunately, the collection is not publicly available.
3 Mining BPMN on GitHub.com
Mining software repositories is a data mining task, consisting of steps of defining
a research objective, selecting and extracting appropriate data, preprocessing
and data cleansing, data analysis, and finally interpreting the analysis results.
In the first step of our implemented data mining pipeline, compare with
Fig. 1, we got a list of all software repositories on GitHub.com by querying a
local instance of the GHTorrent 2 database. We then randomly selected a subset
2
http://ghtorrent.org/
28 Thomas S. Heinze et al.
of 6,163,217 non-forked repositories. All 6,163,217 repositories were examined for
potential BPMN process model artifacts using the GitHub API 3 in the second
step. To this end, the default branch and its file structure were queried for
each repository. Potential BPMN process model artifacts were then identified
by searching for the term "bpmn" in their file name and file extension. Among
the analyzed repositories, we found 1,251 repositories, with at least one potential
BPMN process model artifact and overall 21,306 artifacts. We downloaded the
identified repositories and artifacts. In the third step, since the artifacts included
a wide range of file formats, we filtered for BPMN 2.0’s XML-based serialization
format, which lowered the number of artifacts to 16,907. Additionally removing
duplicates4 , yielded the corpus of 8,904 distinct BPMN 2.0 models. All the
BPMN artifacts were finally subject to a preliminary analysis in the fourth step.
Information on the corpus and analysis outcomes are available online [7]5 .
4 Preliminary Analysis
In our preliminary analysis, we were mainly interested in the diversity of the
found BPMN process model artifacts. We here sketch some of the results. Looking
at the artifacts’ age, more than each third was modified in the last year at the
time of conducting our research. We though also found artifacts older than 8
years. Using the locations of repository contributors allowed us to reason on the
artifacts’ geographical origin, where China, USA, and Germany played prominent
roles. The corpus spans a range of different model sizes. While half of the process
models are smaller than 20 nodes, we also identified 57 models with more than
1,000 nodes. We were also able to confirm the finding reported in [5], that models
play a rather static role in software repositories. Up to three quarter of all the
BPMN process model artifacts were thus never updated at all.
Since the design of BPMN process models is known to be error-prone, we
were also interested in the number of errors found in the models and the need
for analysis tools to help process designers in avoiding those. Various analysis
tools have been developed in recent years, ranging from simple linters [4], over
tools based on data flow analysis [6,8], to full-fledged model checkers [3]. Note
that most of the tools are evaluated using case studies or artifical process models.
Therefore, evaluating analysis tools using our corpus of 8,904 BPMN process
models allows to verify existing tool evaluations based upon a complimentary
empirical means. We have chosen the linting tool BPMNspector 6 [4] for checking
process models with respect to their compliance with the BPMN 2.0 standard [1].
Running the linter revealed violations of the standard’s rules for almost all of the
process models in the corpus. Only 1,471 models were identified as valid BPMN
process models, thus confirming the results for the case study used to evaluate
BPMNspector in [4], which found 42 invalid among overall 66 BPMN models.
3
https://developer.github.com/v3
4
http://doubles.sourceforge.net
5
https://github.com/ViktorStefanko/BPMN_Crawler
6
https://github.com/uniba-dsg/BPMNspector
BPMN in the Wild 29
5 Conclusion
In this paper, we introduced our approach of systematically extracting a corpus of
BPMN business process models from software repositories hosted on GitHub.com.
Mining a fraction of 10% of all software repositories, at the time of conducting our
research, resulted in 8,904 distinct serialized BPMN 2.0 process models. We believe
that our corpus of BPMN models provides a starting point for understanding
more about the practice of BPMN. Note though the general limitations of the
idea of repository mining [10]. In future work, besides increasing the coverage of
analyzed software repositories, we want to research on questions about BPMN’s
use on GitHub.com, e.g., what are frequently and rarely used constructs or are
there certain characteristics that can be used to predict modeling errors [11].
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