Abstract
Process mining techniques are meant to extract non-trivial information from complex data. Controlled experiments of the algorithms underlying process mining techniques often require logs of process executions that fit the specific purposes of each specific test. Therefore, many tools for the log generation from both procedural models (e.g., Petri nets or BPMN models) and declarative models (e.g., based on LTLf or Declare) have been developed. However, the log generation from declarative models still lacks tools for log generation that address specific purposes such as the specification of trace length distributions, the setting of the number of variants that should appear in the log, or the specification of the number of activations of a constraint that should be contained in a trace. We address this research gap by proposing an extension of the Declare4Py Python library that generates synthetic event logs using an Answer Set Programming-based solution whose flexibility supports the encoding of specific purposes.