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Willem Mertens

dblp:34/9191 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2018
0000-0002-1635-3041ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Software engineering, systems software and programming languages · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Software engineering, system software, and programming languages
1 paper
Software testing · 100%
Theoretical computer science
1 paper
Logic in computer science · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Software testing › protocol testing
conformance checking
0.312018
Complete and Interpretable Conformance Checking of Business Processes · IEEE Trans. Software Eng. 2018
Logic in computer science › concurrency theory
concurrency models
0.112018
Complete and Interpretable Conformance Checking of Business Processes · IEEE Trans. Software Eng. 2018
Logic in computer science › concurrency theory
event structures
0.112018
Complete and Interpretable Conformance Checking of Business Processes · IEEE Trans. Software Eng. 2018

Methods — techniques the papers use, named apart from their topics

natural language verbalization · 0.7error-correcting synchronized product · 0.7
YearPublicationVenuePosition
2018 Complete and Interpretable Conformance Checking of Business Processes
abstract
This article presents a method for checking the conformance between an event log capturing the actual execution of a business process, and a model capturing its expected or normative execution. Given a process model and an event log, the method returns a set of statements in natural language describing the behavior allowed by the model but not observed in the log and vice versa. The method relies on a unified representation of process models and event logs based on a well-known model of concurrency, namely event structures. Specifically, the problem of conformance checking is approached by converting the event log into an event structure, converting the process model into another event structure, and aligning the two event structures via an error-correcting synchronized product. Each difference detected in the synchronized product is then verbalized as a natural language statement. An empirical evaluation shows that the proposed method can handle real datasets and produces more concise and higher-level difference descriptions than state-of-the-art conformance checking methods. In a survey designed according to the technology acceptance model, practitioners showed a preference towards the proposed method with respect to a state-of-the-art baseline.
Luciano García-Bañuelos, Nick R. T. P. van Beest, Marlon Dumas, Marcello La Rosa, Willem Mertens
IEEE Trans. Software Eng.5