23,655 research outputs found
Towards Symbolic Model-Based Mutation Testing: Combining Reachability and Refinement Checking
Model-based mutation testing uses altered test models to derive test cases
that are able to reveal whether a modelled fault has been implemented. This
requires conformance checking between the original and the mutated model. This
paper presents an approach for symbolic conformance checking of action systems,
which are well-suited to specify reactive systems. We also consider
nondeterminism in our models. Hence, we do not check for equivalence, but for
refinement. We encode the transition relation as well as the conformance
relation as a constraint satisfaction problem and use a constraint solver in
our reachability and refinement checking algorithms. Explicit conformance
checking techniques often face state space explosion. First experimental
evaluations show that our approach has potential to outperform explicit
conformance checkers.Comment: In Proceedings MBT 2012, arXiv:1202.582
Metamodel-based model conformance and multiview consistency checking
Model-driven development, using languages such as UML and BON, often makes use of multiple diagrams (e.g., class and sequence diagrams) when modeling systems. These diagrams, presenting different views of a system of interest, may be inconsistent. A metamodel provides a unifying framework in which to ensure and check consistency, while at the same time providing the means to distinguish between valid and invalid models, that is, conformance. Two formal specifications of the metamodel for an object-oriented modeling language are presented, and it is shown how to use these specifications for model conformance and multiview consistency checking. Comparisons are made in terms of completeness and the level of automation each provide for checking multiview consistency and model conformance. The lessons learned from applying formal techniques to the problems of metamodeling, model conformance, and multiview consistency checking are summarized
Conformance Checking with Constraint Logic Programming: The Case of Feature Models
Developing high quality systems depends on developing high quality models. An important facet of model quality is their consistency with respect to their meta-model. We call the verification of this quality the conformance checking process. We are interested in the conformance checking of Product Line Models (PLMs). The problem in the context of product lines is that product models are not created by instantiating a meta-model: they are derived from PLMs. Therefore it is usually at the level of PLMs that conformance checking is applied. On the semantic level, a PLM is defined as the collection of all the product models that can be derived from it. Therefore checking the conformance of the PLM is equivalent to checking the conformance of all the product models. However, we would like to avoid this naïve approach because it is not scalable due to the high number of models. In fact, it is even sometimes infeasible to calculate the number of product models of a PLM. Despite the importance of PLM conformance checking, very few research works have been published and tools do not adequately support it. In this paper, we present an approach that employs Constraint Logic Programming as a technology on which to build a PLM conformance checking solution. The paper demonstrates the approach with feature models, the de facto standard for modeling software product lines. Based on an extensive literature review and an empirical study, we identified a set of 9 conformance checking rules and implemented them on the GNU Prolog constraints solver. We evaluated our approach by applying our rules to 50 feature models of sizes up to 10000 features. The evaluation showed that our approach is effective and scalable to industry size models
Decomposing conformance checking on Petri nets with data
Process mining techniques relate observed behavior to modeled behavior, e.g., the automatic discovery of a Petri net based on an event log. Process mining is not limited to process discovery and also includes conformance checking. Conformance checking techniques are used for evaluating the quality of discovered process models and to diagnose deviations from some normative model (e.g., to check compliance). Existing conformance checking approaches typically focus on the control flow, thus being unable to diagnose deviations concerning data. This paper proposes a technique to check the conformance of data-aware process models. We use so-called "data Petri nets" to model data variables, guards, and read/write actions. Additional perspectives such as resource allocation and time constraints can be encoded in terms of variables. Data-aware conformance checking problem may be very time consuming and sometimes even intractable when there are many transitions and data variables. Therefore, we propose a technique to decompose large data-aware conformance checking problems into smaller problems that can be solved more efficiently. We provide a general correctness result showing that decomposition does not influence the outcome of conformance checking. Moreover, two decomposition strategies are presented. The approach is supported through ProM plug-ins and experimental results show that significant performance improvements are indeed possible
Conformance checking: A state-of-the-art literature review
Conformance checking is a set of process mining functions that compare
process instances with a given process model. It identifies deviations between
the process instances' actual behaviour ("as-is") and its modelled behaviour
("to-be"). Especially in the context of analyzing compliance in organizations,
it is currently gaining momentum -- e.g. for auditors. Researchers have
proposed a variety of conformance checking techniques that are geared towards
certain process model notations or specific applications such as process model
evaluation. This article reviews a set of conformance checking techniques
described in 37 scholarly publications. It classifies the techniques along the
dimensions "modelling language", "algorithm type", "quality metric", and
"perspective" using a concept matrix so that the techniques can be better
accessed by practitioners and researchers. The matrix highlights the dimensions
where extant research concentrates and where blind spots exist. For instance,
process miners use declarative process modelling languages often, but
applications in conformance checking are rare. Likewise, process mining can
investigate process roles or process metrics such as duration, but conformance
checking techniques narrow on analyzing control-flow. Future research may
construct techniques that support these neglected approaches to conformance
checking
Process Mining Meets Visual Analytics: The Case of Conformance Checking
Conformance checking is a major function of process mining, which allows organizations to identify and alleviate potential deviations from the intended process behavior. To fully leverage its benefits, it is important that conformance checking results are visualized in a way that is approachable and understandable for non-expert users. However, the visualization of conformance checking results has so far not been widely considered in research. Therefore, the goal of this paper is to develop an understanding of how conformance checking results are visualized by process mining tools to provide a foundation for further research on this topic. We conduct a systematic study, where we analyze the visualization capabilities of nine academic and seven commercial tools by means of a visual analytics framework. In this study, we find that the ``Why?'' aspect of conformance checking visualization seems already be well-defined, but the ``What?'' and ``How?'' aspects require future research
Process Mining Meets Visual Analytics: The Case of Conformance Checking
Conformance checking is a major function of process mining, which allows
organizations to identify and alleviate potential deviations from the intended
process behavior. To fully leverage its benefits, it is important that
conformance checking results are visualized in a way that is approachable and
understandable for non-expert users. However, the visualization of conformance
checking results has so far not been widely considered in research. Therefore,
the goal of this paper is to develop an understanding of how conformance
checking results are visualized by process mining tools to provide a foundation
for further research on this topic. We conduct a systematic study, where we
analyze the visualization capabilities of nine academic and seven commercial
tools by means of a visual analytics framework. In this study, we find that the
''Why?'' aspect of conformance checking visualization seems already be
well-defined, but the ''What?'' and ''How?'' aspects require future research.Comment: Submitted to HICSS-56 (2023
Conformance Checking Based on Multi-Perspective Declarative Process Models
Process mining is a family of techniques that aim at analyzing business
process execution data recorded in event logs. Conformance checking is a branch
of this discipline embracing approaches for verifying whether the behavior of a
process, as recorded in a log, is in line with some expected behaviors provided
in the form of a process model. The majority of these approaches require the
input process model to be procedural (e.g., a Petri net). However, in turbulent
environments, characterized by high variability, the process behavior is less
stable and predictable. In these environments, procedural process models are
less suitable to describe a business process. Declarative specifications,
working in an open world assumption, allow the modeler to express several
possible execution paths as a compact set of constraints. Any process execution
that does not contradict these constraints is allowed. One of the open
challenges in the context of conformance checking with declarative models is
the capability of supporting multi-perspective specifications. In this paper,
we close this gap by providing a framework for conformance checking based on
MP-Declare, a multi-perspective version of the declarative process modeling
language Declare. The approach has been implemented in the process mining tool
ProM and has been experimented in three real life case studies
Scalable Online Conformance Checking Using Incremental Prefix-Alignment Computation
Conformance checking techniques aim to collate observed process behavior with
normative/modeled process models. The majority of existing approaches focuses
on completed process executions, i.e., offline conformance checking. Recently,
novel approaches have been designed to monitor ongoing processes, i.e., online
conformance checking. Such techniques detect deviations of an ongoing process
execution from a normative process model at the moment they occur. Thereby,
countermeasures can be taken immediately to prevent a process deviation from
causing further, undesired consequences. Most online approaches only allow to
detect approximations of deviations. This causes the problem of falsely
detected deviations, i.e., detected deviations that are actually no deviations.
We have, therefore, recently introduced a novel approach to compute exact
conformance checking results in an online environment. In this paper, we focus
on the practical application and present a scalable, distributed implementation
of the proposed online conformance checking approach. Moreover, we present two
extensions to said approach to reduce its computational effort and its
practical applicability. We evaluate our implementation using data sets
capturing the execution of real processes
Conformance checking in UML artifact-centric business process models
Business artifacts have appeared as a new paradigm to capture the information required for the complete execution and reasoning of a business process. Likewise, conformance checking is gaining popularity as a crucial technique that enables evaluating whether recorded executions of a process match its corresponding model. In this paper, conformance checking techniques are incorporated into a general framework to specify business artifacts. By relying on the expressive power of an artifact-centric specification, BAUML, which combines UML state and activity diagrams (among others), the problem of conformance checking can be mapped into the Petri net formalism and its results be explained in terms of the original artifact-centric specification. In contrast to most existing approaches, ours incorporates data constraints into the Petri nets, thus achieving conformance results which are more precise. We have also implemented a plug-in, within the ProM framework, which is able to translate a BAUML into a Petri net to perform conformance checking. This shows the feasibility of our approach.Peer ReviewedPostprint (author's final draft
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