Unsupervised discovery of intentional process models from event logs

Abstract : Research on guidance and method engineering has highlighted that many method engineering issues, such as lack of flexibility or adaptation, are solved more effectively when intentions are explicitly specified. However, software engineering process models are most often described in terms of sequences of activities. This paper presents a novel approach, so-called Map Miner Method (MMM), designed to automate the construction of intentional process models from process logs. To do so, MMM uses Hidden Markov Models to model users' activities logs in terms of users' strategies. MMM also infers users' intentions and constructs fine-grained and coarse-grained intentional process models with respect to the Map metamodel syntax (i.e., metamodel that specifies intentions and strategies of process actors). These models are obtained by optimizing a new precision-fitness metric. The result is a software engineering method process specification aligned with state of the art of method engineering approaches. As a case study, the MMM is used to mine the intentional process associated to the Eclipse platform usage. Observations show that the obtained intentional process model offers a new understanding of software processes, and could readily be used for recommender systems.
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Ghazaleh Khodabandelou, Charlotte Hug, Rebecca Deneckere, Camille Salinesi. Unsupervised discovery of intentional process models from event logs. 11th Working Conference on Mining Software Repositories, May 2014, Hyderabad, India. pp.282-291, ⟨10.1145/2597073.2597101⟩. ⟨hal-00994197⟩

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