Test-driven declarative process modelling combines process models with test traces and has been introduced as a means to achieve both the flexibility provided by the declarative approach and the comprehensibility of the imperative approach. Open test-driven modelling adds a notion of context to tests, specifying the activities of concern in the model, and has been introduced as a means to support both iterative test-driven modelling, where the model can be extended without having to change all tests, and unit testing, where tests can define desired properties of parts of the process without needing to reason about the details of the whole process. The openness however makes checking a test more demanding, since actions outside the context are allowed at any point in the test execution and therefore many different traces may validate or invalidate an open test. In this paper we combine previously developed static techniques for effective open test-driven modelling for Dynamic Condition Response Graphs with a novel efficient implementation of dynamic checking of open tests based on alignment checking. We illustrate the static techniques on an example based on a real-life cross-organizational case management system and benchmark the dynamic checking on models and tests of varying size.

Object-centric process mining views processes and traces as an interaction between many objects, each with their own life cycle, as opposed to being centred around the concept of a single case. Instead of describing implicit process flows, declarative process modelling focuses on the description of processes as a set of explicit rules or constraints. The declarative Dynamic Condition Response (DCR) Graphs notation has seen wide industry adoption, in particular in the Danish public sector, has seen significant work on the development of methods for the modelling, verification, and enactment of collaborative processes, and has led to the development of the award winning DisCoveR process miner. In this paper we apply object-centric concepts to DCR Graphs modelling and mining, in particular we: (1) show an extension to DCR Graphs that allows capturing of object-centric process relations and (2) introduce a process discovery method for such object-centric DCR Graphs. We showcase these contributions on the BPIC2017 loan application log.

Most contemporary process discovery methods take as inputs only positive examples of process executions, and so they are one-class classification algorithms. However, we have found negative examples to also be available in industry, hence we build on earlier work that treats process discovery as a binary classification problem. This approach opens the door to many well-established methods and metrics from machine learning, in particular to improve the distinction between what should and should not be allowed by the output model. Concretely, we (1) present a verified formalisation of process discovery as a binary classification problem; (2) provide cases with negative examples from industry, including real-life logs; (3) propose the Rejection Miner binary classification procedure, applicable to any process notation that has a suitable syntactic composition operator; (4) implement two concrete binary miners, one outputting Declare patterns, the other Dynamic Condition Response (DCR) graphs; and (5) apply these miners to real world and synthetic logs obtained from our industry partners and the process discovery contest, showing increased output model quality in terms of accuracy and model size.