A Qualitative Comparison of Approaches Supporting Business Process Variability [original]

A Qualitative Comparison of Approaches

Supporting Business Process Variability?

Victoria Torres1, Stefan Zugal2, Barbara Weber2, Manfred Reichert3,

Clara Ayora1, and Vicente Pelechano1

1Universitat Polit`ecnica de Val`encia, Spain

{vtorres,cayora,pele}@pros.upv.es

2University of Innsbruck, Austria

{stefan.zugal,barbara.weber}@uibk.ac.at

3University of Ulm, Germany

manfred.reichert@uni-ulm.de

Abstract. The increasing adoption of Process-Aware Information Sys-

tems, together with the reuse of process knowledge, has led to the emer-

gence of process model repositories with large process families, i.e., col-

lections of related process model variants. For managing such related

model collections two types of approaches exist. While behavioral ap-

proaches take supersets of variants and derive a process variant by hiding

and blocking process elements, structural approaches take a base process

model as input and derive a process variant by applying a set of change

operations to it. However, at the current stage no framework for assess-

ing these approaches exists and it is not yet clear which approach should

be better used and under which circumstances. Therefore, to give first

insights about this issue, this work compares both approaches in terms

of understandability of the produced process model artifacts, which is

fundamental for the management of process families and the reuse of

their contained process fragments. In addition, the comparison can serve

as theoretical basis for conducting experiments as well as for fostering

the development of tools managing business process variability.

1 Introduction

The increasing adoption of Process-Aware Information Systems (PAISs) by in-

dustry has led to large collections of process models in a variety of application

domains. Despite the particularities found in specific domains, many of these

models share common parts of their definition (e.g., activities). We denote such

related process variant models as process family in the following.

To properly handle large process families (i.e., avoid redundancies, foster

reusability, and reduce modeling efforts) several proposals have been developed

in recent years (e.g., [1], [2], [3]), which can be classified as either behavioral

or structural approaches. Behavioral approaches represent all members of the

?This work has been developed with the support of MICINN under the project EV-

ERYWARE TIN2010-18011.

2 Victoria Torres et al.

family within the same model artifact, capturing both, commonalities and par-

ticularities of all process variants. In turn, structural approaches use different

artifacts to represent the family e.g., by using a base model to which structural

changes such as inserting, deleting, or moving activities may be applied to derive

process variants. To foster reusability of process model families, understandabil-

ity of the created artifacts is essential. So far, however, no experimental insights

regarding quality aspects (e.g., understandability) are available and it is not clear

under which circumstances the use of one approach is more appropriate than the

other. Since the results of assessing a process model’s understandability signifi-

cantly depend on the specific understandability tasks [4, 5], we have structured

the comparison of both approaches along a specific comprehension task, i.e., the

extraction of a process variant from a configurable model, elaborating on the

process followed by a model reader to accomplish such task. In addition, we use

cognitive psychology as a tool for explaining the differences between the two

approaches. This comparison will provides us the theoretical basis for conduct-

ing experiments as well as for fostering the development of tools for managing

variability in business processes.

Sect. 2 presents a process family from the film industry. Sect. 3 provides

basic notions and introduces the behavioral and structural approaches. Sect.

4 describes concepts from cognitive psychology that will be used in Sect. 5 to

assess their understandability. Sect. 6 then presents an overview of related work.

Finally, Sect. 7 concludes the paper and gives an outlook.

2 Example of a Process Family

As running example, we consider a modified version of a process family from the

film industry for editing a screen project, which varies depending on the shooting

media and delivery media used [6]. First, footage is received, either in tape,film,

or tape and film and prepared for edition depending on the shooting medium.

Then offline edition is performed. Next, the cut stage is performed through

online edit (if the shooting medium is tape), through negmatching (in the case

of film), or through both cuts (when shooting media is tape and film). After

this point, the finishing on a delivery medium phase starts. For this purpose,

the delivery media (e.g., tape, film, tape and film, or new medium) must be

chosen. Now, depending on the delivery medium chosen different variants exist.

For example, when shooting media is film or tape and film and cutting has been

performed through negmatching, a finish on film has to be performed to maintain

the quality of the delivery medium. On the contrary, if the cutting is performed

through online editing and film or tape and film is the delivery medium, record

digital film master needs to be mandatorily performed to transfer the editing

results to film. Similarly, telecine transfer will be performed only if negmatching

is performed previously and the expected delivery format is tape or new medium.

Finally, if neither tape or film have been chosen activity finish on new medium

must be performed.

Qualitative Comparison 3

3 Approaches for Modeling BP Variability

To properly represent variability in a BP model, it is important to define (1)

what parts of the BP model may vary according to a specific context, (2) what

alternatives exist in each of those parts, and (3) which conditions make these al-

ternatives being selected. The first issue refers to the precise identification of the

parts being subject to variation, which are commonly known as variation points.

The second issue refers to the different alternatives that exist for all those varia-

tion points. In addition, some models may require the definition of relationships

(e.g., inclusion, exclusion) between alternative process fragment from different

variation points. The third issue refers to the context of these variations, which

is usually represented by a set of variables gathered in a context model in which

the BP model is used. This subsection presents two different approaches targeted

at the representation of such process families, i.e., behavioural and structural.

Behavioural Approach. The behavioral approach represents a process

family in a single artifact, known as configurable process model capturing both

the commonalities and particularities of the process variants reflecting all possi-

ble behavior. In the following we take C-EPC [2] as the representative proposal

since it constitutes the most well-known and mostly cited proposal. C-EPC ex-

tends EPC with configurable elements (i.e., configurable nodes and configuration

requirements) to explicitly model variability. Fig. 1 illustrates the configurable

process model representing the postproduction process. On the one hand, con-

figurable nodes (i.e., connectors and functions) are represented graphically with

thick solid borders and define variations points in the model where different al-

ternatives may exist. Specifically, configurable functions (e.g., activity telecine

transfer in Fig. 1) can be configured as ON (i.e., function is kept in the model),

OFF (i.e., function is removed from the model), or OPT (i.e., conditional branch-

ing is included in the model deferring the decision to run-time. Configurable con-

nectors, in turn, can be configured to an equally or more restrictive connector.

For example, a configurable OR can configured as a regular OR (not apply-

ing any restrictions), or can restrict its behaviour by configuring it as an XOR

(i.e., selecting one of the outgoing/incoming alternatives), AND (i.e., selecting

all of the outgoing/incoming alternatives), or SEQn(i.e., reducing the alterna-

tives for the configuration of the connector to just one of its outgoing/incoming

sequences). On the other hand, configuration requirements are graphically repre-

sented as tags attached to configurable nodes and formalize, by means of logical

predicates, domain constraints related to the attached nodes. The configuration

of the node will the be made based on the evaluation of the attached config-

uration requirements (e.g., req. 5 requires that activity edit footage online is

chosen when shooting medium is tape). However, configurable nodes not always

have requirements attached to them (since they are only needed when there is

a constraint regarding their configuration). In this case the configurable node is

transformed into a regular one, maintaining the behaviour of the original con-

nector and deferring the configuration decision to run-time.

Structural Approach. This approach proposes a gradual construction of

the process family by modifying the structure of a specific process variant (called

4 Victoria Torres et al.

Prepare

tape for edit

Tape shoot

finished Film shoot

finished

Footage

prepared for

edit

Edit

finished

Post-

production

finished

Requirement 16

(OR5= ‘SEQ5a’

Finish on tape= ‘OFF’) ->

Finish on new medium = ‘ON’

SEQ2a SEQ2b

SEQ3a SEQ3b

SEQ5a SEQ5b

Requirement 13

Record digital film master = ‘ON’ <->

(OR3= ‘SEQ3a’

(OR5= ‘AND’ OR5= ‘SEQ5b’))

Requirement 14

Telecine transfer = ‘ON’ <->

(OR3= ‘SEQ3b’

(Finish on tape = ‘ON’

Finish on new medium = ‘ON’))

Requirement 9

delivery_media = ‘FILM’ ->

(OR5= ‘SEQ5b’)

Requirement 11

Prepare film

for edit

Edit footage

offline

Edit footage

online Perform

negmatching

Finish on

film

Record

digital film

master

Finish on

new

medium

Receive

footage

Shooting

finished

Requirement 4

OR2= OR1

SEQ1a SEQ1b

SEQ4a SEQ4b

Requirement 15

OR6= OR5

Requirement 8

OR4= OR3

Configurable

function

Configurable

OR connector

Configuration

requirement

Function

Event

Requirement 2

shooting_media = ‘FILM’ ->

(OR1= ‘SEQ1b’)

Requirement 1

shooting_media = ‘TAPE’

-> (OR1= ‘SEQ1a’)

SEQ6a SEQ6b

Finish on

tape

Telecine

transfer

Requirement 3

shooting_media = ‘TAPE FILM’

-> (OR1= ‘AND’)

delivery_media = ‘NEW MEDIUM’

-> (OR5= ‘SEQ5a’

Finish on tape = ‘OFF‘)

Requirement 12

Requirement 10

delivery_media = ‘TAPE FILM’ ->

(OR5= ‘AND’

Finish on tape = ‘ON‘)

delivery_media = ‘TAPE’ ->

(OR5= ‘SEQ5a’

Finish on tape = ‘ON‘)

Requirement 5

OR1= ‘SEQ1a’ ->

(OR3= ‘SEQ3a’)

Requirement 7

OR1= ‘AND’ ->

(OR3= ‘AND’)

Requirement 6

OR1= ‘SEQ1b’ ->

(OR3= ‘SEQ3b’)

Fig. 1. C-EPC model for the screen postproduction process

base model) at specific points (i.e., variation points) through change operations.

Following this approach, we find proposals such as Provop [1] or Rule representa-

tion and processing [3]. In the following we use Provop as representative for the

structural approach, since it can be considered the most widely used proposal

for this approach. Fig. 2 illustrates the process family representing the screen

postproduction process using Provop. Provop allows creating process variants

by adjusting the base model (cf. top part of Fig. 2) by the application of a set

of high-level change operations between a couple of adjustment points. Further-

more, Provop allows for more complex configuration adjustments by grouping

multiple change operations into so-called change options (e.g., option 1 com-

bines 2 delete and 2 insert operations). Change options are associated with a

Qualitative Comparison 5

context rule, which is used at configuration time to decide, whether a certain

option is applicable for the given context (e.g., regarding option 1 the context

rule states that this option should only be applied if shooting media is tape). In

addition, Provop allows for an explicit representation of different dimensions of

the context (i.e., context variables and allowed values) by means of the context

model. Finally, to prevent the derivation of semantically invalid variants, Provop

provides the constraint model which allows defining inclusion,exclusion,order

of application,hierarchy, and cardinality relationships between change options

(e.g., the application of option 1 excludes the application of option 2 ).

Prepare

film for edit

Edit footage

offline

Perform

negmatching

Finish on

film

Option 1 Option 2excludes

Option 5 Option 6

Option 7

excludes

excludes excludes

Change options

Options constraints

Context model

shooting_media

delivery_media

Context Variable Range of Values Static/dynamic

static

‘TAPE’, ‘FILM’, ‘TAPE ˄ FILM’, ‘NEW_MEDIA’

AB CDEFG

Adjustment point

Option 2

XOR Prepare tape

for edit

XOR Edit footage

online

CTXT RULE (static):

IF shooting_media = ‘TAPE FILM’

AXZ B

CTXT RULE (static):

IF delivery_media = ‘TAPE’

Option 5

INSERT

DELETE Finish on

film

Finish on

tape

Option 6

CTXT RULE (static):

IF delivery_media = (‘TAPE’ ‘FILM’)

XOR Finish on

tape

CTXT RULE (static):

IF delivery_media = ‘NEW_MEDIA’

Option 7

INSERT

Finish on

new medium

CTXT RULE (static):

IF (shooting_media = ‘FILM’

shooting_media = ‘TAPE FILM‘)

(delivery_media = ‘TAPE’

delivery_media = ’NEW_MEDIA’ )

Option 3

INSERT Telecine

transfer

CTXT RULE (static):

IF (shooting_media = ‘TAPE’

shooting_media = ‘TAPE FILM‘)

delivery_media = ‘FILM’

Option 4

INSERT Record

digital film

master

CTXT RULE (static):

IF shooting_media = ‘TAPE’

Option 1

INSERT

DELETE Prepare

film for edit

Prepare

tape for

edit

DELETE

Perform

negmatching

INSERT

Edit footage

online

Base process

‘TAPE’, ‘FILM’, ‚TAPE ˄ FILM’

DELETE

Finish on film

Fig. 2. Provop model for the screen postproduction process

4 Concepts from Cognitive Psychology

In order to discuss differences between C-EPC and Provop, we will make use

the concepts of external memory,abstraction, and split-attention effect from

cognitive psychology and transfer them to the domain of BP variability.

Basically, three different problem-solving “programs” or “processes” are

known from cognitive psychology: search, recognition, and inference [7]. Search

and recognition allow identifying information of rather low complexity, i.e., lo-

cating an object or recognizing patterns. Most models, however, go well be-

yond complexity that can be handled by search and recognition. For instance,

a Boolean expression certainly cannot be interpreted just by looking at it and

without deliberate thought. Here, the human brain as “truly generic problem

solver” [8] comes into play. Thereby, cognitive psychology differentiates between

working memory that contains the information is currently processed, as well

as long-term memory in which information can be stored for a long period of

time [9]. Most severe, and thus of high interest and relevance, are the limita-

tions of the working memory. As reported in [10], working memory cannot hold

more than 7±2 items at the same time. In this context, the concept of mental

effort, i.e., the amount of working memory used, is of interest, as it can be used

6 Victoria Torres et al.

to assess understanding. As discussed in [11], higher mental effort is in general

associated with lower understanding, or more generally, errors are more likely

to occur when the working memory’s limits are exceeded [12]. Instead of only

measuring accuracy or time for assessing the understandability of a notation,

measuring mental effort allows for a more fine-grained analysis. In particular,

when differences with respect to understandability are small, accuracy may not

change, even though the mental effort changes significantly (cf. [13]). Subse-

quently, we will discuss three factors that influence the required mental effort:

external memory,abstraction, and the split-attention effect.

External Memory. First, we would like to introduce a mechanism known

for reducing mental effort, i.e., the amount of working memory slots in use. An

external memory is referred to any information storage outside the human cog-

nitive system, e.g., pencil and paper or a blackboard [12, 8, 14, 7]. Information

taken from the working memory and stored in an external memory is then re-

ferred to as cognitive trace. In the context of a diagram, a cognitive trace would

be, for instance, to mark, update, and highlight information [14]. Likewise, in the

context of process variants, the model itself may serve as external memory. For

example, when deriving a process variant in C-EPC for a particular context, the

model reader may cross out model elements that have been removed (not requir-

ing her anymore to store them in the working memory). Rather, this information

is transferred to the C-EPC model, freeing up working memory capacity.

Abstraction. Basically, the idea of abstraction is to hide information by ag-

gregation. As irrelevant information can be hidden from the reader, it becomes

easier to focus on relevant information, i.e., abstraction supports the human

mind’s attention management [7], leading to decreased mental effort. Unlike in

C-EPC, where the process family is represented in a single model, Provop sepa-

rates the base model from change options and change options are abstracted via

variation points. This, in turn, simplifies both the base model and the change op-

tions, presumably making both easier to interpret, as attention is not distracted

by an abundance of modeling elements.

Split-Attention Effect. Even though abstraction has been attributed to

reduce mental effort [11, 15, 16], it typically co-occurs with the split-attention

effect, which is known to increase mental effort [17]. In general, the split-attention

effect occurs whenever information from different sources needs to be integrated.

As the human mind can only focus on a single aspect at the same time [18], atten-

tion needs to be constantly switched between the information sources, leading

to increased mental effort. In addition, the task of integrating information is

known to further increase mental effort [17]. To illustrate the split-attention ef-

fect, consider the change options in the Provop approach. Therein, the model

reader has to switch attention between the base model and the change options.

When extracting a process variant for a specific context, the model reader has to

integrate information from change options, i.e., which model elements to change,

with the base model, further increasing mental effort.

Qualitative Comparison 7

5 Qualitative Comparison

So far we have discussed C-EPC and Provop as representatives of approaches

for modeling BP variability. In the following, we will use concepts from cogni-

tive psychology to systematically assess differences between these two proposals

with respect to understandability. Understandability not only depends on the

notation, but also on the type of task to be performed [4, 5]. Due to space re-

strictions we focus in this paper on a specific understandability task, namely the

extraction of a process variant from a configurable process model given a certain

context. To illustrate the process in both proposals, we assume that a model

reader wants to derive the process variant that relates to the production of a

low-budget project which implies the use of tape as medium for both shooting

and delivery tasks. For a description of additional tasks we refer the reader to

http://www.pros.upv.es/technicalreports/PROS-TR-2012-03.pdf

In our qualitative comparison, we assume a setting where the model reader

has the models available in paper-based form. We are aware that, even though

our discussion focuses on cognitive aspects only, tool support is indispensable for

working with configurable models. However, to be able to develop effective tool

support, it is essential to know what makes configurable process models hard to

understand. Without a profound discussion, as provided here, tool development

is rather driven by speculation than by systematic consideration.

In the following, we provide a discussion, first for C-EPC and afterwards for

Provop, structured along the following points: First, we will describe the steps

a model reader has to perform in order to perform the understandability task.

This descriptions have been derived in an iterative manner by observing a set

of model readers conducting the task. Second, we will perform an analysis to

determine the cognitive complexity of the task.

5.1 Extracting a Process Variant Using C-EPC

To obtain the process variant that relates to the low-budget project in C-EPC

(i.e., when shooting and delivery medium is tape) the model reader starts with

the configuration of configurable connector 1. For this purpose, the model reader

evaluates all requirements attached to it, i.e., reqs. 1-7. According to the given

context (i.e., shooting media is tape), configurable connector 1 is configured as

SEQ1aas stated in req. 1. Next, the configuration of configurable connector 2

has to be performed. In this case, req. 4 determines that the same configuration

performed to configurable connector 1 should be applied to configurable connec-

tor 2, i.e., SEQ2ais chosen. Then, the configuration of configurable connector 3

has to be done. For such purpose, reqs. 5–8 and 13 are evaluated. After eval-

uating req. 5 the model reader discovers that SEQ3ashould be chosen. Unlike

reqs. 1–3, reqs. 4–8 and 13–16 are entirely expressed in terms of the structure

of the model, not providing any information regarding the process variant be-

ing configured. This means that the model reader, in order to understand why

this configuration is performed, has to either remember the decisions previously

taken or go back in the model and revisit the requirements that determined the

8 Victoria Torres et al.

configuration of related nodes. Similarly to the configuration performed for con-

figurable connector 2, req. 8 determines that configurable connector 4 should be

configured equally to connector 3, i.e., as SEQ4a. Regarding configurable con-

nector 5, six requirements are attached to it, i.e., reqs. 9-13, and 15. In this case,

the model reader discovers by evaluating req. 9 that it should be configured as

SEQ5aand that function finish on tape should be switched ON. The fact that

these requirements include context variables in it help the model reader to better

understand which configuration should be taken. Next, the model reader has to

decide about the configuration of function telecine transfer. In this case req. 14

states that function telecine transfer should be configured as OFF (since connec-

tor 3 was configured as SEQ3a). Then, configurable connector 6 is configured as

SEQ6aaccording to req. 15. Finally, function finish on new medium is switched

OFF since function finish on tape has been switched ON (req. 16).

Considering the cognitive complexity the use of C-EPC entails we can

identify three basic operations: locating elements, evaluating Boolean expres-

sions, and adapting the model accordingly. As argued in [19], the more distinct

properties a visual element has, e.g., shape and color, the easier it is to identify.

In C-EPC, requirements are represented by white tags, configurable connectors

are represented by white circles with a thick border, whereas configurable func-

tions are represented by green rounded rectangles with a thick border. Hence,

the reader can draw on two different properties (i.e., color and shape) for iden-

tifying distinct modeling constructs presumably requiring a low mental effort.

For identifying whether there are requirements attached to a configurable node,

the model reader can rely on pattern recognition [7] to efficiently perform this

operation (requirements are connected via dotted lines). The first real challenge

occurs when the model reader has to evaluate associated Boolean expressions. As

they can be arbitrarily complex and have to be interpreted in the model reader’s

mind, presumably a high mental effort can be expected. In C-EPC, some re-

quirements are expressed in terms of the structure of the alternatives and not by

the semantics of the process variants being described (e.g., reqs. 4-8, 13–16). In

addition, the configuration of the configurable node being evaluated can depend

on the configuration of previous and/or succeeding related configurable nodes.

This requires a bigger cognitive effort by the model reader, since the model

reader has to remember decisions taken for already configured nodes and might

have to anticipate the configuration of succeeding nodes. For example, when

evaluating req. 14 for configuring function telecine transfer, the model reader

has to go back to the related configurable nodes, i.e., to configurable connector

3, and consider the configuration of functions finish on tape and finish on new

medium to understand the semantics of the configuration. Having evaluated the

requirements respective model adaptations have to be performed (e.g., remov-

ing model elements). Eventhough these operations are rather simple the model

reader has to keep track of all changes made during the configuration. Due to

the limited nature of working memory (7±2 slots), it seems essential that the

model reader can offload parts of the working memory to an external memory,

e.g., by annotating a print-out of the model.

Qualitative Comparison 9

5.2 Extracting a Process Variant Using Provop

To extract a process variant in Provop the model reader first examines all change

options including their associated context rules to determine which options are

applicable for the given context. Based on this, the model reader then selects

options 1 and 5, since only these two satisfy the given context and applies them

to the based model. For this the model reader checks the constraints between

the selected options and concludes that both option 1 and option 5 can be

applied. The application of option 1 involves deleting activity prepare film for

edit between variation points A and B, and inserting activity prepare tape for edit

instead. In addition, activity perform negmatching is replaced by activity edit

footage online. Moreover, the application of option 5 implies the replacement of

activity finish on film by finish on tape between adjustment points E and F.

Considering the cognitive complexity of Provop we can identify two main

operations. First, selecting appropriate change options and second, applying

these change options to the model. For the identification of relevant change op-

tions the model reader inspects all change options and evaluates whether they are

applicable for the current context, i.e., the model reader evaluates the Boolean

expression associated with the change option. Similar to C-EPC, it can be ex-

pected that the interpretation of such Boolean expressions presumably imposes

a high mental effort. However, unlike C-EPC, in Provop Boolean expressions are

always expressed in terms of context variables. These variables provide seman-

tics to the change options, helping the model reader to understand the intent of

the options. After having identified relevant change options, the model reader

has to apply them to the base model. Before applying a change option, it has

to be checked whether the change option is conflicting with previously applied

change options. This task, however, can easily be accomplished using Provop’s

option constraints, i.e., a set of relationships (e.g., inclusion, exclusion) between

change options targeted to ensure their proper use based on the semantics of

the domain. As these option constraints are visually depicted, the model reader’s

recognition capabilities will help to efficiently identify conflicting options, hence

presumably imposing a low mental effort. Regarding the actual manipulation of

the model, the effort for integrating the change options into the base model is

determined by the change distance [20] between the base model and the variant

to be derived. In other words, the more modeling elements are added to / re-

moved from the base model, the more complex the integration task will be. In

addition, the type of change operations contained in the change options influ-

ences complexity. For example, when deleting an activity, respective parts can

be removed from the model by hiding them with a finger on the print-out of

the model, or by crossing them out using a pen. In this way, the model reader

does no longer have to keep this information in his working memory. Rather, the

model serves as external memory, freeing up mental resources. When inserting

activities, in turn, this possibility is not available (except this is done through a

supporting modeling tool) and has to be done in the reader’s mind. Therefore,

complexity grows with the number of changes applied to the base model. There-

fore, an optimized design of the base model that requires minimum changes to

10 Victoria Torres et al.

derive different variants presumably requires less effort by the model reader. Al-

together it can be said that most mental effort will presumably be required for

evaluating Boolean expressions as well as conducting model changes.

5.3 Discussion

After studying both proposals three major differences can be observed. First, in

a C-EPC model, modeling elements are mainly removed from the configurable

model (with exception of functions when these are configured as optional, which

involve the inclusion of some branching condition in the model). By contrast,

in Provop model elements can either be added, deleted, or moved during the

configuration process. As argued above, the cognitive complexity depends on the

type of change operations to be performed (i.e., deletion operations presumably

involve less cognitive effort than insertions or movements).

Second, requirements and configurable nodes are integrated in C-EPC,

whereas change options and the base model are separated in Provop. Similar

to [15, 16], we argue that for small models, C-EPC presumably is easier to un-

derstand, as all the information is integrated and hence in contrast to Provop

no split-attention effect can be expected. However, when model size increases,

models may quickly become too complex resulting in an overload for the model

reader, especially when there are many relationships between alternative model-

ing elements. Here, it can be assumed that the abstraction mechanisms provided

in Provop (i.e., represented by change operations defined separately from the

base model) contributes to retain understandability even for large models.

Third, even though Boolean expressions need to be evaluated in both ap-

proaches, the way they are used by the two proposals differs. In C-EPC, one

of the biggest challenges faced by the model reader relates to the fact that al-

ternatives are usually expressed at the structural level, neglecting the semantics

associated to the different alternatives. This fact involves that, in some cases, the

model reader has to evaluate the Boolean expression at hand, but additionally

has to keep track of previously made decisions. In contrast, in Provop, Boolean

expressions are always expressed in terms of context variables, which contribute

to better understand the semantics of the associated change operations. In ad-

dition, the concept of options (i.e., grouping of related change operations) as

provided by Provop and the explicit specification of constraints between them

in the constraint model presumably reduces the mental effort required by the

reader for understanding them. Hence, from this point of view, Provop models

presumably impose a lower mental effort on average.

6 Related Work

Several proposals have been developed to deal with business process variability

(e.g., [1, 2, 3]). These works take a design-oriented perspective and provide tech-

nical solutions for managing variability; the understandability of the artifacts

Qualitative Comparison 11

created using such approaches is not in the focus. Recently qualitative evalua-

tions of the C-EPC proposal in form of case studies have been conducted [21, 22].

However, to the best of our knowledge no work has addressed understandability

of process model families explicitly. Closely related is, however, existing empirical

research on the understandability of process models. Most approaches thereby

employ the concept of metrics computed on structural aspects of the process

model to assess understandability, e.g., [23, 24, 25]. While metrics seem to be a

promising approach to assess model complexity and understandability, in [4, 5]

it is shown that understandability of a process model significantly depends on

the type of question asked. Consequently, a metric will only be able to roughly

estimate understandability. In [26, 15, 16], concepts from cognitive psychology

are used as a tool to discuss understandability of process models for specific

comprehension tasks. In this paper we build upon this work, and extend it to

discuss understandability of configurable process models.

7 Summary and Outlook

The main goal of this paper is to compare the structural and behavioral ap-

proaches for modeling process model families in terms of understandability. In-

stead of looking at understandability from a broad perspective, the discussion is

centered around the extraction task of a process variant from the modeling arti-

facts produced by C-EPC and Provop. The different approaches are discussed in

terms of different concepts from cognitive psychology based on which the men-

tal effort required to understand the modeling artifacts produced by the two

approaches can be estimated. In turn, this allows us to estimate the understand-

ability of the two approaches for specific comprehension task. The task addressed

in this paper constitutes just a first attempt regarding the investigation of under-

standability of these two approaches. Formal metrics and experiments involving

a large number of subjects and different configurable process models are planned

as future work to empirically test the discussion. Based on the comprehension

tasks presented in this paper, we will conduct a series of experiments to em-

pirically assess the understandability of both approaches and to investigate the

factors that impact understandability of process model families improving the

modeling of such families and facilitating their reuse.

References

1. Hallerbach, A., Bauer, T., Reichert, M.: Capturing variability in business process

models: the Provop approach, J. Soft. Maintenance. 22(6–7):519–546 (2010)

2. Rosemann, M., van der Aalst, W.M.P.: A configurable reference modeling language.

Inf. Systems 32(1):1–23 (2007)

3. Kumar, A., Wen, Y.: Design and management of exible process variants using tem-

plates and rules. Int. J. Comput. Ind. 63(2), pp. 112–130 (2012).

4. Figl, K., Laue, R.: Cognitive Complexity in Business Process Modeling. In Proc.

CAiSE’11, 452–466.

12 Victoria Torres et al.

5. Melcher, J., Detlef, S.: Towards Validating Prediction Systems for Process Under-

standability: Measuring Process Understandability. In Proc. SYNASC’08, 564–571.

6. La Rosa, M., Lux, J., Seidel, S., Dumas, M., ter Hofstede, A.H.M.: Questionnaire-

driven Configuration of Reference Process Models. In Proc. CAiSE’07, 424-438.

7. Larkin, J.H., Simon, H.A.: Why a Diagram is (Sometimes) Worth Ten Thousand

Words. Cognitive Science, 11(1):65–100 (1987).

8. Tracz, W. J.: Computer programming and the human thought process. Software:

Practice and Experience, 9(2):127–137 (1979).

9. Paas, F., Tuovinen, J.E., Tabbers, H., Van Gerven, P.W.M.: Cognitive Load Mea-

surement as a Means to Advance Cognitive Load Theory. Educational Psychologist,

38(1):63–71 (2003).

10. Miller, G.: The Magical Number Seven, Plus or Minus Two: Some Limits on Our

Capacity for Processing Information. The Psychological Review, 63(2):81–97 (1956).

11. Moody, D. L.: Cognitive Load Effects on End User Understanding of Conceptual

Models: An Experimental Analysis. In Proc. ADBIS’04, 129–143.

12. Sweller, J.: Cognitive load during problem solving: Effects on learning. Cognitive

Science, 12(2):257–285 (1988).

13. Zugal, S., Pinggera, J., Weber, B.: The Impact of Testcases on the Maintainability

of Declarative Process Models. In Proc. BPMDS’11, 163–177.

14. Scaife, M., Rogers, Y.: External cognition: how do graphical representations work?

Int. J. Human-Computer Studies, 45(2):185–213 (1996).

15. Zugal, S., Pinggera, J., Mendling, J., Reijers, H.A., Weber, B.: Assessing the Im-

pact of Hierarchy on Model Understandability-A Cognitive Perspective. In Proc.

EESSMod’11, 123–133.

16. Zugal, S., Soffer, P., Pinggera, J., Weber, B.: Expressiveness and Understandabil-

ity Considerations of Hierarchy in Declarative Business Process Models. In Proc.

BPMDS’12, 167–181.

17. Sweller, J., Chandler, P.: Why Some Material Is Difficult to Learn. Cognition and

Instruction, 12(3):185–233 (1994).

18. Feldmann Barrett, L., Tugade, M. M., Engle, R. W.: Individual Differences in

Working Memory Capacity and Dual-Process Theories of the Mind, Psychol Bull.

130(4):553-573 (2004).

19. Moody, D.L.: The “Physics” of Notations: Toward a Scientific Basis for Construct-

ing Visual Notations in Software Engineering. IEEE Trans. Soft. Eng. 35(6):756–779

(2009).

20. Li, C., Reichert, M., Wombacher, A.: On Measuring Process Model Similarity

Based on High-Level Change Operations. In Proc. ER’08, 248–264.

21. L¨onn, C.M., Uppstr¨om, E., Wohed, P., Juell-Skielse, G.: Configurable Process Mod-

els for the Swedish Public Sector. In Proc. CAiSE’12, 190–205.

22. Gottschalk, F., Wagemakers, T., Jansen-Vullers, M., van der Aalst, W., La Rosa,

M., van Eck, P., Gordijn, J., Wieringa, R.: Configurable Process Models: Experiences

from a Municipality Case Study. In Proc. CAiSE’09, 486–500.

23. Mendling, J., Reijers, H.S., Cardoso. J.:What Makes Process Models Understand-

able? In Proc. BPM’07, 48–63.

24. Vanderfeesten, I., Reijers, H.A., van der Aalst, W.M.P.: Evaluating workflow pro-

cess designs using cohesion and coupling metrics. Int. J. Comput. Ind. 59(5):420–437

(2008).

25. Reijers, H.A., Mendling, J.: A Study into the Factors that Influence the Under-

standability of Business Process Models. SMCA 41(3):449–462 (2011).

26. Zugal, S., Pinggera, J., Weber, B.: Assessing Process Models with Cognitive Psy-

chology. In Proc. EMISA’11, 177–182.