Process Mining Applied in a Software Project Development with SCRUM and ProM



Abstract Views
362

Downloads
173

Citations

Share

##plugins.themes.bootstrap3.article.sidebar##

Submitted Jul 31, 2023
Published Sep 18, 2023
10.24018/ejeng.2023.8.5.3089

Abstract viewed = 362 times

Table of Contents

##plugins.themes.bootstrap3.article.main##

The software product is high used by the society in general and its development complexity are inputs of this research that gears into the software development processes. The mapping and modelling of software processes, as well as their standardization are not trivial tasks in the industry of software. Therefore, process mining practices can be useful for discovering or validating processes. This article presents a hypothetical software development project that uses the agile SCRUM method, Jira software, Jenkins pipeline and a process mining tool called ProM. As the project team generates many records using the software development tools, these records are considered event logs and it is be used during process mining activities. ProM allows users to identify processes from the event logs and is used with the records generated by Jira and Jenkins. The visualization of a possible process derived from the use of these event logs is presented when using the ProM tool and the Flexible Heuristics Miner algorithm. In conclusion, process mining can be useful to discover or validate existing software processes during the execution of a software project, also allowing these processes to be standardized to be used in future projects.

Keywords: Agile Process Mining ProM SCRUM Method Software Project

Downloads

Download data is not yet available.

References

  1. Abrahamsson P, Salo O, Ronkainen J, Warsta J. Agile software development methods: Review and analysis. arXiv preprint arXiv:1709.08439. 2017 Sep 25. doi: 10.48550/arXiv.1709.08439.
     Google Scholar
  2. van Eck ML, Lu, X, Leemans SJ, van der Aalst WMP. PM2: a process mining project methodology. Advanced Information Systems Engineering. CAiSE 2015. Springer, Cham. 2015. doi: 10.1007/978-3-319-19069-3_19.
    DOI  |   Google Scholar
  3. Lemos AM, Sabino CC, Lima RMF, Oliveira CAL. Using process mining in software development process management: A case study. 2011 IEEE International Conference on Systems, Man, and Cybernetics, IEEE, pp. 1181-1186, Anchorage, AK, USA, 2011. doi: 10.1109/ICSMC.2011.6083858.
    DOI  |   Google Scholar
  4. van der Aalst WMP, Adriansyah A, De Medeiros AKA, Arcieri F, Baier T, Blickle T, et al. Process mining manifesto. Business Process Management Workshops: BPM 2011 International Workshops, Springer Berlin Heidelberg, pp. 169-194, Clermont-Ferrand, France, 2012. doi: 10.1007/978-3-642-28108-2_19.
    DOI  |   Google Scholar
  5. Rubin V, Günther CW, van der Aalst WMP, Kindler E, van Dongen BF, Schäfer W. Process mining framework for software processes. International Conference on Software Process, ICSP 2007: Software Process Dynamics and Agility, Springer, Berlin, Heidelberg, pp. 169-181, Minneapolis, MN, USA, 2007 May 19-20. doi.org/10.1007/978-3-540-72426-1_15.
    DOI  |   Google Scholar
  6. Poncin W, Serebrenik A, van den Brand M. Process Mining Software Repositories. 15th European Conference on Software Maintenance and Reengineering, Oldenburg, Germany, pp. 5-14, 2011. doi: 10.1109/CSMR.2011.5.
    DOI  |   Google Scholar
  7. Keith B, Vega V. Process mining applications in software engineering. Trends and Applications in Software Engineering, CIMPS 2016, Advances in Intelligent Systems and Computing, Springer, Cham, vol 537, 2017. doi:10. 10.1007/978-3-319-48523-2_5.
    DOI  |   Google Scholar
  8. Fernandez-Llatas C, Lizondo A, Monton E, Benedi J-M, Traver V. Process Mining Methodology for Health Process Tracking Using Real-Time Indoor Location Systems. Sensors 2015;15(12):29821–40. doi: 10.3390/s151229769.
    DOI  |   Google Scholar
  9. Gupta M, Serebrenik A, Jalote P. Improving software maintenance using process mining and predictive analytics. 2017 IEEE International Conference on Software Maintenance and Evolution (ICSME), IEEE, pp. 681-686, Shanghai, China, 2017. doi: 10.1109/ICSME.2017.39.
    DOI  |   Google Scholar
  10. Kalliamvakou E, Gousios G, Blincoe K, Singer L, German DM, Damian D. The promises and perils of mining github. Proceedings of the 11th Working Conference on Mining Software Repositories, pp. 92-101, 2014 May. doi: 10.1145/2597073.2597074.
    DOI  |   Google Scholar
  11. Larrea VGV, Joubert W, Fuson CB. Use of Continuous Integration Tools for Application Performance Monitoring [Internet]. 2015. Available from: https://www.osti.gov/biblio/1185993.
     Google Scholar
  12. Seth N, Khare R. ACI (Automated Continuous Integration) using Jenkins: Key for Successful Embedded Software Development. International Conference on Recent Advances in Engineering & Computational Sciences (RAECS), IEEE, pp. 1-6, Chandigarh, India, 2015. doi: 10.1109/RAECS.2015.7453279.
    DOI  |   Google Scholar
  13. Ali M., Khan SU, Vasilakos AV. Security in cloud computing: Opportunities and challenges. Information Sciences. 2015; 305: 357-383. doi: 10.1016/j.ins.2015.01.025.
    DOI  |   Google Scholar
  14. Filion L, Daviot N, Le Bel JP, Gagnon M. Using Atlassian tools for efficient requirements management: An industrial case study. Annual IEEE International Systems Conference (SysCon), IEEE, pp. 1-6, Montreal, QC, Canada, 2017, doi: 10.1109/SYSCON.2017.7934769.
    DOI  |   Google Scholar