We present our efforts in creating and analyzing a corpus of BPMN process models by mining software repositories. Systematically searching for BPMN 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

Amme, Wolfram

Heinze, Thomas

Stefanko, Viktor

English

Institute of Transport Research:Publications

BPMN in the Wild: BPMN on GitHub.comThomas S. Heinze1, Viktor Stefanko2, and Wolfram Amme21 German Aerospace Center (DLR)thomas.heinze@dlr.de2 Friedrich Schiller University Jena[wolfram.amme,viktor.stefanko]@uni-jena.deAbstract. We present our efforts in creating and analyzing a corpus ofBPMN process models by mining software repositories. Systematicallysearching for BPMN process artifacts in 6,163,217 repositories or 10%of all repositories hosted on GitHub.com, at the time of conducting ourresearch, resulted in a diverse corpus of 8,904 BPMN 2.0 process models.1 IntroductionWithin the last years, an increasing number of software projects have shiftedtowards using platforms such as GitHub.com for their software development. Usingthese platforms as a source of data for empirical research allows for addressing awide range of questions on the practice of software development and receives moreand more attention, as indicated by the popularity of the flagship conference onthe topic: International Conference on Mining Software Repositories (MSR)1.Research in the domain of business process modeling can as well benefit fromsuch a data-driven approach. Due to characteristics of the domain, i.e., “processequals product”, there is a lack of larger and commonly available datasets withreal-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 suchas GitHub.com, can help to overcome this lack and provides a complimentaryapproach to empirical research besides existing methods like case studies, experi-ments, and surveys. For example, research questions on how a language such asthe Business Process Model and Notation (BPMN) [1] is used in practice can beaddressed, in order to differentiate between the frequently and the rarely usedparts of the language, thus advancing language and tool development. Analyzingmodeling styles furthermore allows for investigating best practices and guidelinesto help process designers. Eventually, best practices and tools as proposed byacademic research or industry can be evaluated more realistically [12].In this paper, we present our approach for mining software repositories onGitHub.com to create and analyze a corpus of BPMN process models. Due tothe sheer number of repositories on GitHub.com and time constraints, we limitedour approach to a randomly selected subset of 6,163,217 repositories or 10% of1 http://www.msrconf.org  Repository  SelectionGHTorrent GitHubDataExtraction Filtering / CleansingGitHubAPI v31 2 3 4   AnalysisFig. 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 modelswhich are defined using BPMN 2.0’s XML-based serialization format.2 Related WorkThe Lindholmen dataset has been an inspiration for this paper [5,12]. In Hebig etal. [5], the authors describe their approach to mine GitHub.com for UML modelsand 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 modelinglanguages while BPMN is one domain-specific modeling language. We are notaware of other work, which mines software repositories for BPMN models.There have also been community efforts to create model collections [9]. TheBPM Academic Initiative provides a platform to create and share businessprocess 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 andthe focus is on conceptual models as most models originate from students. Asimilar platform has been introduced last year under the name RePROSitory [2],including 174 business process models in its current database. Another initiativeis the BenchFlow project, where business process models were collected fromindustrial partners. The authors claim to have collected 8,363 models, with ashare of 64% of BPMN [13]. Unfortunately, the collection is not publicly available.3 Mining BPMN on GitHub.comMining software repositories is a data mining task, consisting of steps of defininga research objective, selecting and extracting appropriate data, preprocessingand data cleansing, data analysis, and finally interpreting the analysis results.In the first step of our implemented data mining pipeline, compare withFig. 1, we got a list of all software repositories on GitHub.com by querying alocal instance of the GHTorrent2 database. We then randomly selected a subset2 http://ghtorrent.org/of 6,163,217 non-forked repositories. All 6,163,217 repositories were examined forpotential BPMN process model artifacts using the GitHub API 3 in the secondstep. To this end, the default branch and its file structure were queried foreach repository. Potential BPMN process model artifacts were then identifiedby searching for the term "bpmn" in their file name and file extension. Amongthe analyzed repositories, we found 1,251 repositories, with at least one potentialBPMN process model artifact and overall 21,306 artifacts. We downloaded theidentified repositories and artifacts. In the third step, since the artifacts includeda wide range of file formats, we filtered for BPMN 2.0’s XML-based serializationformat, which lowered the number of artifacts to 16,907. Additionally removingduplicates4, yielded the corpus of 8,904 distinct BPMN 2.0 models. All theBPMN 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 AnalysisIn our preliminary analysis, we were mainly interested in the diversity of thefound BPMN process model artifacts. We here sketch some of the results. Lookingat the artifacts’ age, more than each third was modified in the last year at thetime of conducting our research. We though also found artifacts older than 8years. Using the locations of repository contributors allowed us to reason on theartifacts’ geographical origin, where China, USA, and Germany played prominentroles. The corpus spans a range of different model sizes. While half of the processmodels are smaller than 20 nodes, we also identified 57 models with more than1,000 nodes. We were also able to confirm the finding reported in [5], that modelsplay a rather static role in software repositories. Up to three quarter of all theBPMN process model artifacts were thus never updated at all.Since the design of BPMN process models is known to be error-prone, wewere also interested in the number of errors found in the models and the needfor analysis tools to help process designers in avoiding those. Various analysistools have been developed in recent years, ranging from simple linters [4], overtools based on data flow analysis [6,8], to full-fledged model checkers [3]. Notethat most of the tools are evaluated using case studies or artifical process models.Therefore, evaluating analysis tools using our corpus of 8,904 BPMN processmodels allows to verify existing tool evaluations based upon a complimentaryempirical means. We have chosen the linting tool BPMNspector 6 [4] for checkingprocess 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 theprocess models in the corpus. Only 1,471 models were identified as valid BPMNprocess models, thus confirming the results for the case study used to evaluateBPMNspector in [4], which found 42 invalid among overall 66 BPMN models.3 https://developer.github.com/v34 http://doubles.sourceforge.net5 https://github.com/ViktorStefanko/BPMN_Crawler6 https://github.com/uniba-dsg/BPMNspector5 ConclusionIn this paper, we introduced our approach of systematically extracting a corpus ofBPMN business process models from software repositories hosted on GitHub.com.Mining a fraction of 10% of all software repositories, at the time of conducting ourresearch, resulted in 8,904 distinct serialized BPMN 2.0 process models. We believethat our corpus of BPMN models provides a starting point for understandingmore about the practice of BPMN. Note though the general limitations of theidea of repository mining [10]. In future work, besides increasing the coverage ofanalyzed software repositories, we want to research on questions about BPMN’suse on GitHub.com, e.g., what are frequently and rarely used constructs or arethere certain characteristics that can be used to predict modeling errors [11].References1. Business Process Model and Notation (BPMN), Version 2.0. Object ManagementGroup (OMG) Standard (2011), https://www.omg.org/spec/BPMN/2.0/PDF2. Corradini, F., Fornari, F., Polini, A., Re, B., Tiezzi, F.: RePROSitory: a RepositoryPlatform for Sharing Business PROcess modelS. In: BPM PhD/Demos 2019. pp.149–153. CEUR (2019)3. Fahland, D., Favre, C., Jobstmann, B., Koehler, J., Lohmann, N., Vo¨lzer, H., Wolf,K.: Instantaneous Soundness Checking of Industrial Business Process Models. In:BPM 2009. pp. 278–293. Springer (2009)4. Geiger, M., Neugebauer, P., Vorndran, A.: Automatic Standard Compliance Assess-ment of BPMN 2.0 Process Models. In: ZEUS 2017. pp. 4–10. CEUR (2017)5. Hebig, R., Quang, T.H., Chaudron, M., Robles, G., Fernandez, M.A.: The Questfor Open Source Projects that use UML: Mining GitHub. In: MODELS 2016. pp.173–183. ACM (2016)6. Heinze, T.S., Amme, W., Moser, S.: Static analysis and process model transformationfor an advanced business process to Petri net mapping. Softw.: Pract. & Exp. 48(1),161–195 (2018)7. Heinze, T.S., Stefanko, V., Amme, W.: Mining von BPMN-Prozessartefakten aufGitHub. In: KPS 2019. pp. 111–120. DHBW Stuttgart (2019)8. Heinze, T.S., Tu¨rker, J.: Certified Information Flow Analysis of Service Implemen-tations. In: SOCA 2018. pp. 177–184. IEEE (2018)9. Ho-Quang, T., Chaudron, M.R.V., Robles, G., Herwanto, G.B.: Towards an In-frastructure for Empirical Research into Software Architecture: Challenges andDirections. In: ECASE@ICSE 2019. pp. 34–41. IEEE (2019)10. Kalliamvakou, E., Gousios, G., Blincoe, K., Singer, L., German, D.M., Damian,D.E.: The Promises and Perils of Mining GitHub. In: MSR 2014. pp. 92–101. ACM(2014)11. Kunze, M., Luebbe, A., Weidlich, M., Weske, M.: Towards Understanding ProcessModeling – The Case of the BPM Academic Initiative. In: BPMN 2011 Workshops.pp. 44–58. Springer (2011)12. Robles, G., Ho-Quang, T., Hebig, R., Chaudron, M., Fernandez, M.A.: An extensivedataset of UML models in GitHub. In: MSR 2017. pp. 519–522. IEEE (2017)13. Skouradaki, M., Roller, D., Leymann, F., Ferme, V., Pautasso, C.: On the Road toBenchmarking BPMN Workflow Engines. In: ICPE 2015. pp. 301–304. ACM (2015)

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