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Discovering configuration workflows from existing logs using process mining

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Abstract

Variability models are used to build configurators, for guiding users through the configuration process to reach the desired setting that fulfils user requirements. The same variability model can be used to design different configurators employing different techniques. One of the design options that can change in a configurator is the configuration workflow, i.e., the order and sequence in which the different configuration elements are presented to the configuration stakeholders. When developing a configurator, a challenge is to decide the configuration workflow that better suits stakeholders according to previous configurations. For example, when configuring a Linux distribution the configuration process starts by choosing the network or the graphic card and then, other packages concerning a given sequence. In this paper, we present COnfiguration workfLOw proceSS mIning (COLOSSI), a framework that can automatically assist determining the configuration workflow that better fits the configuration logs generated by user activities given a set of logs of previous configurations and a variability model. COLOSSI is based on process discovery, commonly used in the process mining area, with an adaptation to configuration contexts. Derived from the possible complexity of both logs and the discovered processes, often, it is necessary to divide the traces into small ones. This provides an easier configuration workflow to be understood and followed by the user during the configuration process. In this paper, we apply and compare four different techniques for the traces clustering: greedy, backtracking, genetic and hierarchical algorithms. Our proposal is validated in three different scenarios, to show its feasibility, an ERP configuration, a Smart Farming, and a Computer Configuration. Furthermore, we open the door to new applications of process mining techniques in different areas of software product line engineering along with the necessity to apply clustering techniques for the trace preparation in the context of configuration workflows.

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Notes

  1. BPMN: Business Process Model and Notation

  2. Dendrogram that is a branching diagram which represents the arrangement of the clusters produced by the corresponding analyses

  3. https://fluxicon.com/disco/

  4. https://www.celonis.com/

  5. https://doi.org/10.5281/zenodo.3574053

  6. https://wwwiti.cs.uni-magdeburg.de/~jualves/PROFilE/datasets-download/Dell-Laptop_readme.txt

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Acknowledgements

This work has been partially by the Ministry of Science and Technology of Spain through ECLIPSE (RTI2018-094283-B-C33) and OPHELIA (RTI2018-101204-B-C22) projects; the TASOVA network (MCIU-AEI TIN2017-90644-REDT); and the Junta de Andalucía via METAMORFOSIS projects, the European Regional Development Fund (ERDF/FEDER), and the MINECO Juan de la Cierva postdoctoral program.

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    Belén Ramos-Gutiérrez, Ángel Jesús Varela-Vaca, José A. Galindo, María Teresa Gómez-López & David Benavides

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Appendix: Quality metrics results

Appendix: Quality metrics results

This appendix contains in the Tables 9, 10, 11, 12, and 13, the metric data represented in Figs. 10, 11, 12. To facilitate the interpretation of the data, the values of the metrics have been normalised in each metric, so that, all the results are between 0 and 1, allowing comparisons to be made. In addition, Table 8 is included to show the metric values for the original logs. With this, it can be seen how, in most cases, their values are closer to 0 after clustering, meaning that the resulting configuration workflows are also less complex. Still, it is important to note that it is very difficult to determine a generalisation regarding this data, since they are too domain-specific.

Table 8 Metrics for the initial logs of each case study
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Table 9 Metrics for ERP case with entropy-features
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Table 10 Metrics for ERP case with entropy-transitions
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Table 11 Metrics for Smart Farm case with entropy-features
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Table 12 Metrics for Computer Configuration case with entropy-features
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Table 13 Metrics for Computer Configuration case with entropy-transitions
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Ramos-Gutiérrez, B., Varela-Vaca, Á.J., Galindo, J.A. et al. Discovering configuration workflows from existing logs using process mining. Empir Software Eng 26, 11 (2021). https://doi.org/10.1007/s10664-020-09911-x

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