=Paper=
{{Paper
|id=Vol-2605/8
|storemode=property
|title=Change Patterns for Decision Model and Notation (DMN) Model Evolution
|pdfUrl=https://ceur-ws.org/Vol-2605/8.pdf
|volume=Vol-2605
|authors=Faruk Hasić,Estefanía Serral
|dblpUrl=https://dblp.org/rec/conf/benevol/HasicS19
}}
==Change Patterns for Decision Model and Notation (DMN) Model Evolution==
https://ceur-ws.org/Vol-2605/8.pdf
Change Patterns for Decision Model and Notation
(DMN) Model Evolution
Faruk Hasić and Estefanı́a Serral
Research Centre for Information Systems Engineering (LIRIS)
KU Leuven, Brussels, Belgium
{estefania.serralasensio, faruk.hasic}@kuleuven.be
which depicts the requirements of decisions and the
dependencies between elements involved in the deci-
Abstract sion model. Second, the decision logic level, which
presents ways to specify the underlying decision logic.
Information systems rely on decision knowl-
The DMN standard employs rectangles to depict de-
edge during their execution. A recently intro-
cisions and sub-decisions, and ovals to represent data
duced standard, the Decision Model and No-
input. The decision logic level is usually represented in
tation (DMN), has been adopted in both in-
the form of decision tables. An example of a DRD di-
dustry and academia as a suitable method for
agram deciding on the severity of Chronic Obstructive
modelling decision knowledge. However, this
Pulmonary Disease (COPD) in a patient is provided in
decision knowledge is not static and may un-
Figure 1, while Figure 2 depicts the top-level decision
dergo changes after system deployment. DMN
logic in the form of a decision table.
change patterns have not yet been studied in
the literature. This paper fills this gap by pre- Although it is obvious that the reasoning of deci-
senting an initial set of DMN change patterns. sions can change over time to adapt to changing re-
The patterns presented in this paper will not quirements, all current works approach DMN from a
only facilitate the understanding of decision static perspective, i.e., no attention has been given to
change management, but can also be capi- changing or adaptable decision models. In this paper
talised on for, among other things, adapting we approach this research gap by identifying a first
decision management systems to be more flex- set of change patterns that can be applied to DMN
ible, consistency checking of decision models, decision models. These findings can aid in developing
and developing modelling tools that facilitate and implementing dynamic decision management tools
those changes. and systems that meet the requirements of flexible and
changing decision knowledge.
Index terms— Decision Model and Notation This paper is structured as follows. Section 2 con-
(DMN), Model Evolution, Change Patterns stitutes a related work section. In Section 3 runtime
evolution of decision models is explained, while pos-
1 Introduction sible decision model change patterns are discussed in
Decision Model and Notation (DMN) is a recently Section 4. Section 5 deals with future research. Fi-
introduced decision modelling standard that has en- nally, Section 6 concludes the paper.
joyed significant interest in literature [1, 2, 3, 4, 5].
DMN consists of two levels that are to be used in con- 2 Related Work
junction. First, the decision requirement level repre-
sented by the Decision Requirement Diagram (DRD) Most works around DMN have focused on the inte-
gration of process and decision models from a mod-
Copyright © by the paper’s authors. Use permitted under Cre- elling point of view e.g., [2, 6, 7, 8, 9]. Others focus
ative Commons License Attribution 4.0 International (CC BY
4.0).
on the automatic discovery of decision model from en-
In: D. Di Nucci, C. De Roover (eds.): Proceedings of the 18th
riched process event logs [10, 11]. Furthermore, litera-
Belgium-Netherlands Software Evolution Workshop, Brussels, ture provides a set of tools for modelling DMN models
Belgium, 28-11-2019, published at http://ceur-ws.org [12, 13, 14].
1
Decision node
(top-level decision)
Decision node
(subdecision)
COPD severeness
Muscle activity Skin temperature
Information
Heart rhythm Respiration requirement
Input data node
EMG data ECG data Respiratory data Skin sensor data
Figure 1: A DMN model for COPD severeness.
Figure 2: A decision table for COPD severeness.
The ability of a knowledge-based system to effi- Decision models can be specified according to the
ciently deal with decision rule changes is considered a DMN standard. The models are saved in a file reposi-
key property in literature [15, 16, 17, 18, 19, 20, 21, 22]. tory as an Extensible Markup Language (XML) file,
This way, business rigidity is avoided and the ability adhering to the XML Metadata Interchange (XMI)
to transform the underlying business rules to new re- standard [24], as specified by DMN 1.2 [1]. Execu-
alities is facilitated. However, existing DMN decision tion engines are capable of interpreting XML files of
model literature addresses decision modelling from a decision models from the file repository, and conse-
static perspective where decision models are built and quently executing the corresponding decisions. Such
used, without any form of model evolution. Change an engine is provided by Camunda [25]. The engine
patterns are often used to define the possible evolu- executes the decisions and consequently the software
tion of models. These change patterns rely on the services related to the decisions. The engine logs the
elementary edit operations that can be applied on the executions for possible future analyses.
model elements, i.e., insertion and deletion, as well The connection between the aforementioned com-
as substitution, which in essence is a combination of ponents is shown in Figure 3. The arrows between the
insertion and deletion [23]. Furthermore, change pat- components represent information flows. Namely, the
terns can help facilitate the understanding of model decision models are fed into the file repository, which
change management as they provide a guide for imple- is then fed to the execution engine for enactment. The
menting changes to models while maintaining model engine executes the decisions and logs the execution.
consistency. Nonetheless, change patterns for decision
Changing the decision model at runtime will re-
models were not yet addressed in the literature.
sult in a new XML specification of the model, i.e.,
a new model variant. Deploying a new model vari-
3 Runtime Decision Evolution ant for interpretation affects all future instances, as
In this section we explain how decision models are used they will follow the newest model variant. Old run-
at runtime. An overview of decision model execution ning instances will continue to run according to the old
is given in Figure 3. In what follows, we take a closer model specification as a result of the variant version-
look at the elements of the architecture and their in- ing mechanism of the engine. This is a straightforward
terrelations. approach to assure that old instances continue to run
2
in a sound way. 3. ∆Π3: Excluding a decision output indicates
the deletion of an existing output from the output
4 Towards Decision Model Change set of a decision table.
Patterns 4. ∆Π4: Including a decision output indicates
To accommodate decision model changes, designers an addition of a new output to the output set of
should be able to evolve the decision models after de- a decision table.
ployment. A number of changes can occur in the de- 5. ∆Π5: A decision logic change indicates a
cision model. The core elements of a decision model change in relating the existing input symbols to
are depicted in Figure 1, i.e. the input data and the the existing output symbols within the decision
decision nodes within a DRD, connected via informa- table, i.e., the mapping from inputs to outputs.
tion requirements arrows. The logic encapsulated in a
decision node is usually modelled with decision tables, 4.2 Change patterns on decision rules in their
such as shown in Figure 2. To determine the change entirety
patterns, we investigate the changes that can mani-
fest themselves on the core DMN elements, provided Next to changes within decision rules, we can view
in the meta-model of the DMN specification [1], at dif- decision rules as an atomic entity and perform changes
ferent levels of granularity. First, we assess the change on the decision rule in its entirety:
patterns within a single decision rule, i.e., changing 1. ∆Π6: Excluding a decision rule if a decision
the inputs and outcomes of a single rule or row in the rule is deemed irrelevant at a certain point in time.
decision table. Next, we look at change patterns for The decision rule can be deleted in its entirety
a decision rule in its entirety, i.e., adding or deleting from a decision table. Notice that by deleting
decision rules from a decision table. These change pat- decision rules, the decision table may not be com-
terns all pertain to a single node of the DRD, i.e., a plete anymore. To avoid this, either the decision
single decision table, according to the DMN decision table should be completed by ensuring that all
table meta-model [1]. Finally, we investigate change input values are mapped to existing decision out-
patterns on the topological structure of the DRD itself, comes, or the system should be redesigned to cap-
respectively the addition and deletion of decision nodes ture the possibility of no decision outcome being
and data input nodes. The change patterns are derived returned.
from the formalisation of core decision model elements
in [2] and the elementary edit operations that can be 2. ∆Π7: Including a decision rule if a new de-
applied on the elements, i.e., insertion and deletion, cision rule is deemed relevant at a certain point
as well as substitution, which in essence is a combina- in time. The decision rule can be added in its
tion of insertion and deletion [23]. Table 1 provides entirety to an existing decision table.
an overview of the change patterns directly relating to
core DMN elements. Table 1: Overview of decision model change patterns.
Decision table change patterns
4.1 Change patterns within decision rules Changes within decision rules
∆Π1 Decision input exclusion.
We indicate a change pattern with ∆Π. For changes ∆Π2 Decision input inclusion.
within decision rules, we can distinguish three ele- ∆Π3 Decision output inclusion.
∆Π4 Decision output exclusion.
ments in the decision table that can undergo changes: ∆Π5 Decision rule logic change.
the inputs, the outputs, and the logic mapping the Changes on decision rules
inputs to the outputs, i.e. the decision rules: ∆Π6 Decision rule exclusion.
∆Π7 Decision rule inclusion.
1. ∆Π1: Excluding a decision input indicates Decision requirements diagram change patterns
deleting an existing input variable from a decision Decision node changes
∆Π8 Decision node exclusion.
table. However, simply deleting an input variable ∆Π9 Decision node inclusion.
can render the decision table to be incomplete and Input data node changes
incorrect. As such, the table may need to undergo ∆Π10 Input data node inclusion.
refactoring in order to render a decision table that ∆Π11 Input data node exclusion.
is complete and correct [26].
4.3 Change patterns on the decision nodes in
2. ∆Π2: Including a decision input indicates
the DRD
adding a new input variable to a decision table.
Similar to ∆Π1, the table may need to undergo This subsection deals with the deletion or addition of
refactoring after this change pattern is applied decision nodes in the decision requirements diagram.
3
File repository Log
Modelling Execution
environment engine
Figure 3: An overview of decision model execution.
1. ∆Π8: Excluding a decision node from the be propagated throughout the entire decision model
DRD corresponds to deleting all decision rules to safeguard within-model consistency. For instance,
from a decision node. Hence, this change pat- deleting an input data node will also require delet-
tern is an aggregation of multiple exclude decision ing the inputs in the decision tables that require the
rule changes (∆Π6). Note that deleting a decision deleted input data node.
node also deletes all its incoming and outgoing in-
formation requirements arrows.
5 Future Work
2. ∆Π9: Including a decision node to the set of
In future work, we will investigate how a change in
decision nodes corresponds to adding a new de-
the decision model can require the triggering of other
cision table, and thus, adding multiple decision
changes in order to safeguard within-model consis-
rules encapsulated in the decision node. Hence,
tency. Additionally, we will investigate how chang-
this change pattern is in essence an aggregation
ing decision models impacts other systems and mod-
of multiple include decision rule changes (∆Π7).
els that rely on the logic encapsulated in the decision
Note that adding a decision node also adds the
models. More specifically, we will examine how the
necessary incoming and outgoing information re-
proposed change patterns influence process and deci-
quirements arrows.
sion model consistency in an integrated process and
decision model environment. Changing the underly-
4.4 Change patterns on the input data nodes
ing decisions of a process can lead to change patterns
in the DRD
in the process model as well if the sound interaction
This subsection deals with the deletion or addition of between the process and decision model is to be en-
input data nodes in the decision requirements diagram. sured.
Furthermore, flexible decision models are of partic-
1. ∆Π10: Including an input data node to the ular interest to Internet-of-Things (IoT) process set-
set of data input nodes also adds its necessary tings [27], as IoT process are inherently subject to
input requirement arrows and connects it to the a dynamic and changeable environment. Therefore,
relevant decision nodes in the DRD. Notice that we will investigate how these change patterns mani-
this change pattern on the DRD level corresponds fest themselves in decision-intensive IoT processes.
to adding a new input variable to the decision Finally, we will look into developing a tool that pro-
table that requires the newly added data input vides possibilities for DMN model evolution with au-
node. Hence, this change pattern is equivalent to tomated model consistency checking and repair, while
∆Π2. maintaining the link with the Camunda execution en-
2. ∆Π11: Excluding an input data node from gine.
the set of data input nodes also deletes all its in-
put requirement arrows. Notice that this change 6 Conclusion
pattern on the DRD level corresponds to deleting
an input variable from the decision table that re- This paper presents an initial set of decision model
quired the input data node. Hence, this change change patterns for the evolution of DMN decision
pattern is again equivalent to ∆Π1. models. We recognise that the adaptation of a DMN
decision model can lead to within-model inconsisten-
Note that adopting a change pattern on a DMN cies and that additional change patterns may need to
decision model can lead to within-model inconsisten- be propagated throughout the entire decision model to
cies and that additional change patterns may need to safeguard within-model consistency.
4
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