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- 1. Metrics for the Case Management Modeling and Notation (CMMN) Speciﬁcation Mike A. Marin Hugo Lotriet John A. Van Der Poll University of South Africa September 30, 2015 SAICSIT 2015 Wallenberg Research Centre, Stellenbosch, South Africa Mike A. Marin, Hugo Lotriet, John A. Van Der Poll Complexity metrics for CMMN
- 2. Outline Agenda Motivation Case Management Modeling and Notation Methodology CMMN Model Modeling elements and annotators Size Metric Length Metric Complexity Metric Findings Future Work Mike A. Marin, Hugo Lotriet, John A. Van Der Poll Complexity metrics for CMMN
- 3. Motivation Business Process Management (BPM) is widely used by businesses to automate business process in the enterprise Extensive research has been conducted in several aspects of the technology Including complexity metrics Commonly used notations are procedural and graph based Nodes represents activities Arcs represent routes The Business Process Management and Notation (BPMN) is the main BPM standard Created by the object management group (OMG) BPMN version 1.0 released in May 2004 The Case Management Modeling and Notation (CMMN) is a new process notation Trying to understand modeling complexity for the CMMN Identify and validate complexity metrics for CMMN Mike A. Marin, Hugo Lotriet, John A. Van Der Poll Complexity metrics for CMMN
- 4. Diﬀerences between traditional BPM and CMMN Traditional BPM CMMN Procedural Declarative Control ﬂow Event based Process centric Data centric Engine control process ﬂow Knowledge workers control the ﬂow Arcs describe the sequence No predeﬁned sequence BPMN Example CMMN Example Mike A. Marin, Hugo Lotriet, John A. Van Der Poll Complexity metrics for CMMN
- 5. Case Management Modeling and Notation (CMMN) CMMN is a new process modeling notation Version 1.0 released in May 2014 Created by the object management group (OMG) Notation Compatible with BPMN Diamonds represent guards (pre-conditions) Rounded rectangles represent tasks Declarative Notation based on business artifacts with guard-stage-milestone CMMN Example Mike A. Marin, Hugo Lotriet, John A. Van Der Poll Complexity metrics for CMMN
- 6. Methodology Extensive literature review on complexity software metrics, in particular for Workﬂow and BPM. Formalized the deﬁnition of CMMN in order to deﬁne metrics and validate them Identify three metrics Size (CS) Length (CL) Complexity (CC) Used the theoretical and empirical validation of software product measures framework deﬁned by Briand et al. to validate the three metrics Used Weyuker's nine properties for evaluating software complexity measures to further validate the Complexity metric (CC) Mike A. Marin, Hugo Lotriet, John A. Van Der Poll Complexity metrics for CMMN
- 7. CMMN Model Deﬁnition (Model) A CMMN model C is deﬁned as a tuple C = E, U, V, A Where E is a set of modeling elements. U is a binary relationship in which two elements x and y in E are related if and only if they are contained in the same scope. V is a binary relationship in which two elements x and y in E are related if and only if an event from one (x) triggers the other (y). A is a set of annotators used to indicate characteristics of elements in E. Mike A. Marin, Hugo Lotriet, John A. Van Der Poll Complexity metrics for CMMN
- 8. Modeling elements E and annotators A Modeling elements E Annotators A Mike A. Marin, Hugo Lotriet, John A. Van Der Poll Complexity metrics for CMMN
- 9. Size Metric Deﬁnition (Size) The size of a model C denoted by CS(C) is deﬁned as the cardinality of E, CS(C) = |E| Example CS(C) = 8 Mike A. Marin, Hugo Lotriet, John A. Van Der Poll Complexity metrics for CMMN
- 10. Size metric characteristics Size CS(C) complies with the Briand et al. framework properties for size metrics Size (Non-negativity) The size of a model S = E, R is non-negative. Size (Null value) The size of a model S = E, R is zero if E is empty. Size (Module additivity) The size of a module S = E, R is equal to the sum of the sizes of two of its modules m1 = Em1, Rm1 and m2 = Em2, Rm2 such that any element of S is in either m1 or in m1. Mike A. Marin, Hugo Lotriet, John A. Van Der Poll Complexity metrics for CMMN
- 11. Length Metric Deﬁnition The length of a model C denoted by CL(C) is deﬁned as the maximum nesting depth of a model. The length CL(C) can be calculated by the following algorithm, Example CL(C) = 3 Mike A. Marin, Hugo Lotriet, John A. Van Der Poll Complexity metrics for CMMN
- 12. Length metric characteristics Length CL(C) complies with the Briand et al. framework properties for length metrics Length (Non-negativity) The length of a model S = E, R is non-negative. Length (Null value) The length of a model S = E, R is zero if E is empty. Length (Non-increasing monotonicity for connected components) Adding relationships between elements of a module m does not increases the length of the model S = E, R . Length (Non-decreasing monotonicity for non-connected components) Adding relationships between the elements of two modules m1 and m2 does not decrease the length of the model S = E, R . Length (Disjoint modules) The length of a model S = E, R made up of two disjoint modules m1 and m2 is equal to the maximum of the lengths of the modules m1 and m2. Mike A. Marin, Hugo Lotriet, John A. Van Der Poll Complexity metrics for CMMN
- 13. Complexity Metric Deﬁnition The complexity of a model C denoted by CC(C) is deﬁned as, CC(∅) = 0, otherwise CC(C) = i∈E∪A Wi Where, the weight, Wi is given in by a table of weights. Example CC(C) = 11 Mike A. Marin, Hugo Lotriet, John A. Van Der Poll Complexity metrics for CMMN
- 14. Complexity metric characteristics Complexity CC(C) complies with the Briand et al. framework properties for complexity metrics Complexity (Non-negativity) The complexity of a model S = E, R must be non-negative. Complexity (Null value) The complexity of a model S = E, R is zero if R is empty. Complexity (Symmetry) The complexity of a model S = E, R does not depend on the convention chosen to represent the relationships between its elements. Complexity (Module monotonicity) The complexity of a model S = E, R is no less than the sum of the complexities of any two of its modules with no relationships in common. Complexity (Disjoint module additivity) The complexity of a model S = E, R composed of two disjoint modules m1 and m2 is equal to the sum of the complexities of the two modules. Mike A. Marin, Hugo Lotriet, John A. Van Der Poll Complexity metrics for CMMN
- 15. Properties 1/2 Complexity CC(C) complies with the Weyuker’s complexity properties Property (Non-coarseness) A metric should not rank all models as equally complex. Property (Granularity) A metric should rank only a ﬁnite number of models with the same complexity. Property (Non-uniqueness) A metric should allow some models to have the same complexity. Property (Design details are important) Two distinct but equivalent models that compute the same function need not have the same complexity. Property (Monotonicity) The complexity of two models joined together is greater than or equal to the complexity of either model considered separate. Mike A. Marin, Hugo Lotriet, John A. Van Der Poll Complexity metrics for CMMN
- 16. Properties 2/2 Complexity CC(C) complies with the Weyuker’s complexity properties Property (Nonequivalence of interaction) Two models with the same complexity when each is joined to a third model the resulting complexity may be diﬀerent between the two. Property (Permutation) Complexity should be responsive to the order of statements. Property (Renaming) Complexity should not be aﬀected by renaming. Property (Interaction may increase complexity) (∃P)(∃Q)( P + Q < P;Q ). Mike A. Marin, Hugo Lotriet, John A. Van Der Poll Complexity metrics for CMMN
- 17. Findings Main ﬁndings The formalization of the CMMN model was suﬃcient to deﬁne and validate the metrics. The three proposed metrics comply with the formal framework for software measurements deﬁned by Briand et al. The complexity metric also complies with the properties described by Weyuker. Both Briand et al., and Weyuker assume that software systems are build using a procedural style, based on directed acyclic graphs that may not be totally applicable to CMMN. Mike A. Marin, Hugo Lotriet, John A. Van Der Poll Complexity metrics for CMMN
- 18. Future work Work is required to understand the applicability of Briand et al., and Weyuker to declarative systems. Work is required to conduct the empirical validation for the proposed metrics. CMMN claims an approach based on business artifacts, therefore further work is required to compare the formal CMMN model described in the paper with a formalization of business artifacts. The weights given for the complexity metric CC(C) were assigned based on the intuition of the authors, and further empirical work is needed to ﬁne tune the weights. Empirical work is needed to understand the inﬂuence of CMMN non-visual entities on the complexity metric. Mike A. Marin, Hugo Lotriet, John A. Van Der Poll Complexity metrics for CMMN
- 19. Thanks Mike A. Marin, Hugo Lotriet, John A. Van Der Poll Complexity metrics for CMMN

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