EVALUATION OF BPMN MODELS QUALITY

A Family of Experiments

Elvira Rolón

Autonomous University of Tamaulipas, Centro Universitario Tampico-Madero, 89336, México

Félix García, Francisco Ruiz, Mario Piattini

Department of Information Technologies and Systems, Indra-UCLM Research and Development Institute

University of Castilla-La Mancha, Paseo de la Universidad 4,13071, Ciudad Real, Spain

Corrado Aaron Visaggio, Gerardo Canfora

RCOST – Research Centre on Software Technology, University of Sannio

Palazzo ex Poste, Viale Traiano, 82100 Benevento, Italy

Keywords: Business process models, BPMN, Empirical validation, Family of experiments, Measures.

Abstract: The design phase is of special importance in the development of a business process. This phase refers to the

modeling, handling and redesigning of processes, but when maintenance tasks have to be performed, this

stage may be rather complicated. It implies a heavy investment of time and resources, since it involves both

technical developers and business analysts. Moreover, process modeling should permit not only the

production of models which are understandable to the users, but also the early detection and correction of

errors. All of this adds to the overall quality of the model. We therefore propose a set of measures with

which to assess the structural complexity of conceptual business process models. Our aim is to obtain useful

indicators to be used when carrying out maintenance tasks on these models, thus obtaining higher quality

models by means of an early evaluation of the model’s given quality properties. With the development of a

family of experiments, it has been possible to discover a set of measures which may be useful in assessing

the usability and maintainability of conceptual business process models.

1 INTRODUCTION

The objectives of a business process are basically

(Multamäki, 2002): a) To improve the understanding

of a situation so that it can then be communicated to

and among the different stakeholders and b) to use

that process as a tool to attain the goals of a process

development project. Nevertheless, for business

processes to be able to comply with their objective,

they are constantly exposed to changes. These come

about as a result of organizations’ continuous

improvement programmes.

Business process modeling consists of the

description and visualization of processes by means

of a model which represents them in formal or

informal ways or in the form of a graph or diagram.

Likewise, the manipulation and redesign process is

carried out in the design phase (Smith and Fingar,

2003). Business processes modeling is therefore one

of the first steps towards achieving organizational

goals. It is an activity which has gained great

importance due to the fact that today’s organizations

are ever-more focused on their business process

(Andersson et al., 2005).

The importance that business process modeling

represents has been the springboard for a variety of

studies such as that of Bandara (Bandara et al.,

2005) in which the authors attempt to identify

process modeling success factors and measures.

Their empirical evidence comes from the case

studies of nine process modeling projects.

Furthermore, business process modeling is of

interest in a number of different fields such as that of

business and software engineering. This is because

its importance lies not only in the description of the

Rolón E., García F., Ruiz F., Piattini M., Aaron Visaggio C. and Canfora G. (2008).

EVALUATION OF BPMN MODELS QUALITY - A Family of Experiments.

In Proceedings of the Third International Conference on Evaluation of Novel Approaches to Software Engineering, pages 56-63

DOI: 10.5220/0001762300560063

 SciTePress

process, but in that it also usually represents a

preparatory phase for activities such as (Succi et al.,

2000): business process improvement, business

process reengineering, technology transfer and

process standardization.

High-quality conceptual-modeling plays an

important role in carrying out business process

reengineering in particular, making it possible to

detect errors at an early stage and thus correct them

(Wand and Weber, 2002). In addition to this, the

analysis of the level of process maturity (Bider,

2005; Francis, 2005), also forces us to have bases

which facilitate modeling in the design phase. This

is also true as regards the work of future

maintenance.

Bearing these factors in mind, and considering

the lack of studies on the possible difficulty that

business process models may represent in

maintenance tasks, our work takes as its main focus

of attention the assessment of the structural

complexity of business process models (BPMs) at a

conceptual level. Our aim is to give support to

business process management, allowing an early

evaluation of certain quality properties of the

models. It also makes the evolution of process

models possible, providing as it does so objective

information about maintainability, especially in

those organizations which have given themselves

over to ongoing improvement.

In this work we present the motivation of our

research, basing it on how important it is to evaluate

business process conceptual models if we are to aid

their maintainability (Section 3). In addition, we

present the results obtained in five experiments

within the context of a family of experiments

(Section 4 and 5). With these results we have

attempted to obtain a set of measures which will

serve as useful indicators towards the usability and

maintainability of the BPMs. Finally, in Section 6

some of the conclusions drawn from this work will

be put forward.

2 RELATED WORKS

The importance that the subject of business process

and its modeling has acquired in the last few years

has also generated great interest in the scientific

community with respect to its study, analysis and

measurement. However, very little can be found in

literature as regards the measurement and

assessment of business processes, at least at a

conceptual level, which is the main topic of our

research.

A recent work on measures of complexity for

business process models is that presented by

(Gruhn

and Laue, 2006), in which the authors discuss how

ideas that are already a familiar part of research into

software complexity might be used to analyse the

complexity of business process models.

On the other hand, the reference by (Cardoso,

2005) describes a measurement for analyzing the

control-flow complexity of Web Processes and

Workflows. This measurement is used at the time of

design to evaluate the complexity of the design of a

process before its implementation.

Having taken into consideration the studies made

in the field of software engineering in (Rolón et al.,

2006b), we have defined a set of measures for the

evaluation of conceptual business process models on

the basis of the adaptation and extension of a

framework defined for the modeling and

measurement of the software process. In (Cardoso et

al., 2006) a similar type of compilation of insights

from software engineering cognitive science and

graph theory is provided, and the authors discuss to

what extent analogous metrics of these areas can be

defined for business. Finally, in (Latva-Koivisto,

2001) a collection of complexity measures for

business process models found in the relevant

literature was compared to a set of given criteria.

3 MEASURES FOR BUSINESS

PROCESS MODELS

Our interest lies in evaluating the complexity of

business processes by starting from the model which

represents them at a conceptual level, and in order to

do this we have used BPMN (OMG, 2006) as a

modeling language. One of the reasons for the use of

BPMN in our proposal is that it is one of the most

widely recognized standard notations for the

modeling of business processes and it is that which

is most often used by both business analysts and

systems analysts.

Moreover, a variety of business process

modeling tools already use the BPMN metamodel

and certain studies, such as that of (Mendling et al.,

2005), show how BPMN, in comparison with

another 14 specifications includes the 15 high-level

metamodel concepts defined by the author, almost in

their entirety. (Wohed et al., 2006) also provide a

comprehensive evaluation of the capabilities of

BPMN and its strengths and weaknesses when used

for business process modeling. These studies, as is

the case of other similar ones found in literature

EVALUATION OF BPMN MODELS QUALITY - A Family of Experiments

(Havey, 2005), give us an indication of the

importance of using this notation.

In order to attain objective knowledge of the

external quality of business process models (BPMs)

we have defined a set of measures with which to

evaluate their structural complexity which is

represented with BPMN. These measures have been

placed in two categories:

• Base measures. These consist principally of

counting the business process model’s significant

elements, and a total of 46 base measures have

been defined according to the main elements of

which the BPMN metamodel is composed

(activities, events, gateways, pools, etc).

• Derived measures. These have been defined

from the base measures, and allow us to discover

the proportions that exist between the different

elements of the model. This group is made up of

a total of 14 measures.

Some of the derived measures defined according to

the base measures are shown in Table 1. A more

detailed description of all the proposed measures

appears in (Rolón et al., 2006b).

Table 1: Derived Measures.

With the defined base and derived measures, it is

possible to evaluate the structural complexity of

business process models developed with BPMN.

When we analyse the model structurally, it is thus

also possible for us to evaluate its internal quality.

The defined measures have been validated

theoretically according to the Briand et al.

theoretical framework (Briand et al., 1995). As a

result, it has been possible to group them in relation

to the various properties of structural complexity

such as size, coupling and complexity, as regards

internal quality

(Figure 1).

Figure 1: Relationship between structural complexity and

quality attributes.

Moreover, in line with our objective, which is

that of discovering which of the defined measures

may provide useful and objective information about

the external quality of the BPMs, we will focus on

two characteristics of the external quality of the ISO

9126: Usability and Maintainability. These will be

evaluated by means of the following two sub-

characteristics which are respectively:

• Understandability. The ease with which the

model can be understood by the user.

• Modifiability. The ease with which the model can

be modified, by possible errors, by requesting a

specific modification or by new requirements.

In order to discover what measures may serve as

useful indicators to evaluate the understandability

and modifiability of the MPNs, a family of

experiments has been carried out, which is described

in the following sections.

4 EMPIRICAL VALIDATION – A

FAMILY OF EXPERIMENTS

The family of experiments includes the development

of 5 experiments which have been carried out in

similar circumstances and in the same context.

The experimental design used was the same for

all 5 experiments, since the second experiment was

planned as a replica of the first, the purpose of this

being to corroborate the results obtained, and the

fourth experiment was similarly a replica of the

third.

The variant of these experiments with respect to

both of the first consists of some changes to the

ENASE 2008 - International Conference on Evaluation of Novel Approaches to Software Engineering

experimental material in the MPNs, with the

intention of confirming whether the measures

validated in both first experiments might or might

not be useful in evaluating the usability and

maintainability of the MPNs. A detailed description

of the experimental design can be consulted in

(Rolón et al., 2006a).

Of the five experiments conducted, it is

important to emphasize that the third was carried out

with the Masters students of the University of

Sannio in Italy, because this implied an additional

effort, which was mainly that of considering the

language as a barrier since the training session was

given to them in English.

In addition, it took longer to carry out this

experiment, since the training was more thorough

with regard to business process modeling and

BPMN modeling notation. Also, subjects such as

Business Process Management and tools such as the

BPMS (Business Process Management Systems)

were mentioned.

4.1 Subjects

The participant subjects in all the experiments had

similar knowledge as far as process modeling was

concerned. All the groups were nevertheless given a

training session to ensure that they were conscious

of the aspects that we were attempting to evaluate. A

summary of the groups who participated in each

experiment can be see in Table 2.

Table 2: Participant groups in the experiments.

4.2 Material

In all cases the material consisted of ten BPMs

represented with BPMN, whose structural

characteristics and dimensions were different from

each other; that is to say, models with different

degrees of complexity were selected. These were

obtained by varying the value of the measures in

each model. Our intention, upon choosing models

with different dimensions, was to determine the

influence of the complexity of the model upon

different subjects such as business analysts and

software engineers, who are the main focus of our

study.

Moreover, two questionnaires were formulated

for each of the aforementioned models. The first

consisted of a series of six questions related to the

model’s understandability, and the second proposed

a series of modifications to be carried out on the

model, such as evaluating the complexity of the

process models presented. In addition, at the end of

each questionnaire a question was included, whereby

the subjects were asked to assess the complexity of

the models presented in a subjective manner. The

material also included an example of a solution

which showed how the exercises should be done. An

example of the material used can be found in (Rolón

et al., 2007).

4.3 Objective

Using the GQM template (Goal Question Metric) the

goal in all the experiments is defined as being:

• To Analyse measures of BPM structural

complexity

• To evaluate them as regards their capability of

being used as indicators of business process

model understandability and modifiability

• The researchers’ point of view

• The context of PhD students, research assistants

and lecturers in software engineering (Exp. 1);

Students of the Masters degree in Information

Systems (Exp. 2); Post graduate students (Exp.

3); Administrative staff and health professionals

(Exp. 4) and PhD Students (Exp. 5).

4.4 Variables and Hypothesis

Within the context of the family of experiments the

same variables have been considered. These are:

• Independent variables: those which correspond

with the proposed measures, which is to say the

base measures and derived measures already

described.

• Dependent variables: those which relate to the

understandability and modifiability of the BPMs,

which will be measured according to the

subjects’ efficiency when performing the tasks,

which is calculated as the ratio between the

number of right answers and the time.

The dependent variables were measured through

the subjects’ response times when carrying out the

required tasks, the success rate in the questions

relating to the understandability and modifiability

EVALUATION OF BPMN MODELS QUALITY - A Family of Experiments

tasks of the model, the subjective evaluation with

respect to the complexity of the models, and also the

efficiency of the successes in relation to the times.

The hypotheses proposed with respect to the

objective of our investigation are the following:

• Null hypothesis, H

: There is no significant

correlation between the structural complexity

measures and the understandability.

• Alternative hypothesis, H

: There is a significant

correlation between the structural complexity

measures and the understandability.

• Null hypothesis, H

: There is no significant

correlation between the structural complexity

measures and the modifiability.

• Alternative hypothesis, H

: There is a

significant correlation between the structural

complexity measures and the modifiability.

5 ANALYSIS OF RESULTS

Once the individual experiments had been carried

out, a global analysis of the results took place within

the context of the family of experiments in order to

determine whether or not they had attained the

general objective of the empirical evaluation. In

order to do this, a descriptive analysis and a

statistical analysis of the data collected in all five

experiments were carried out. A description of both

analyses is presented in the following sections.

5.1 Descriptive Analysis

Having taken into account that the dependant

variables are those which are relative to the

understandability and modifiability of the model, a

summary of the data obtained from the results of

each experiment was carried out.

Each variable was measured according to the

response times, the success rate in the required tasks,

the subjective evaluation that the subjects made, and

the efficiency of the successes in relation to the time.

We shall now present the results which were

obtained after having analyzed each of these aspects.

Table 3 shows a summary of the results obtained

from the experiments which were carried out, with

regard to the time (in minutes) that the subjects

needed to respond to the tasks relating to

understandability and modifiability.

Upon analyzing the time taken by the subjects to

carry out the required tasks and upon obtaining the

mean times of the five experiments, it can be

observed in Table 3 that, in the case of the tasks

relating to the model’s understandability, the

subjects took more time with models 5, 7 and 10,

whilst they took more time to carry out the requested

modifications with models 3, 4 and 7.

Table 3: Answer times.

These results can be better appreciated in Figure

2, in which the results that appear in Table 3 are

grouped according to the average of the results

obtained in each experiment in order to discover

both which models the subjects took most time to

respond to, and the models’ understandability and

modifiability.

Figure 2: Summary of the average times.

The descriptive analysis relating to success,

subjective evaluation and efficiency was carried out

in a similar manner. With regard to success in the

required tasks, the results of the five experiments

show that models 3, 4 and 7 were those which led to

the subjects producing the greatest amount of errors

in the responses related to understandability, whilst

in the tasks related to modifiability the majority of

mistakes were made with models 3, 7 and 10 (Fig.3).

ENASE 2008 - International Conference on Evaluation of Novel Approaches to Software Engineering

Figure 3: Summary of right answers.

As regards the subjects’ subjective evaluation of

the complexity of understandability of the models

presented, models 5, 6, 9 and 10 were evaluated as

being the most complex, whilst in the case of

modifiability the most complex models were 5, 7

and 10 (Figure 4).

Figure 4: Subjective evaluation chart.

In this case, models 5 and 10 coincide in both

tasks as being the most complex models according

to the subjects’ criterium, and these results coincide

with the values of the measurements of each of the

MPNs in which the models of greatest structural

complexity were 7, 9 and 10.

Finally, the efficiency of the successful

responses to the tasks in relation to the time taken to

carry them out was obtained from the statistical

analysis of the dependant variables.

Figure 5 shows the mean results of the five

experiments and, as can be seen, the models which

have the lowest level of efficiency as regards

understandability were 5, 7 and 10. Those which had

the lowest level as regards modifiability were 2, 5

and 7.

Figure 5: Efficiency chart.

5.2 Statistical Analysis

The summary of the mean times, successes,

subjective evaluation and efficiency, both for the

understandability and for the modifiability tasks,

along with the values of the measurements of the

business process models were used to carry out a

statistical analysis.

Initially, a correlation analysis of the values of

the measurements as regards the response times and

the number of successful responses from the results

obtained in the five experiments was carried out by

following the suggestions of (Perry et al., 2000),

(Wohlin et al., 2000), (Juristo and Moreno, 2001),

(Ciolkowski et al., 2002) and (Briand et al., 1995).

In order to prove whether the distribution of the

data obtained was normal, the Kolmogorov-Smirnov

test was applied. As a result of this it was obtained

that the distribution was not normal, and for this

reason we decided to use a non-parametrical

statistical test such as the Spearman correlation

coefficient with a level of significance of α = 0.05

which indicates the probability of rejecting the null

hypothesis when it is certain (type I error). That is to

say, a confidence level of 95% exists.

The Spearman correlation coefficient was used to

separately correlate each of the measurements with

the dependant variables as regards each of the

aspects evaluated in the descriptive analysis.

The results of the correlation analysis of the five

experiments to discover understandability and

modifiability times obtained that the measures which

correlated with the response times for the tasks

relating to understandability, and which were

validated in at least two of the five experiments

were: NIMsE (Number of Intermediate Message

Events), NEDDB (Number of Exclusive Decision

Data-Based), TNIE (Total Number of Intermediate

Events of the model), NSFE (Number of Sequence

EVALUATION OF BPMN MODELS QUALITY - A Family of Experiments

Flows from Events) and TNE (Total Number of

Events of the model).

With regard to modifiability, the NEDEB

(Number of Exclusive Decision Event-Based) and

CLA (Connectivity Level between Activities)

measures were validated in experiments 2 and 3.

The analysis of the correlations with regard to

successes, subjective evaluation and efficiency was

carried out in a similar manner. With regard to the

correlations of the measures as regards successes in

the required tasks, only the TNSE (Total Number of

Start Events of the model) was validated in two of

the five experiments as far as successes in

understandability were concerned. In the case of

successes in the modifiability tasks, of the various

correlation measurements the NDOIn (Number of

Data Object-In of the process) and TNEE (Total

Number of End Events of the model) measures were

only validated in two experiments.

In the efficiency analysis (Table 4), the measures

validated in at least two of the five experiments with

regard to understandability were: NIMsE, NEMsE

(Number of End Message Events), NEDDB, NSFE,

TNE y NSFL. In relation to modifiability, the

following measures were validated: NCS (Number

of Collapsed Sub-processes), TNCS (Total Number

of Collapsed Sub-processes of the model), NEDEB

and CLA.

Table 4: Efficiency correlations.

Finally, upon analyzing the correlations as

regards the subjective evaluation that the subjects

made of the complexity of the models, we obtained

the result that measures which were validated in at

least two of the five experiments only existed in the

case of modifiability.

These measures were: TNE, TNA (Total Number

of Activities), NENE (Number of End None Events),

NT (Number of Tasks), NSFL, TNEE, TNT (Total

Number of Tasks in the model), NEDDB, NSFG

(Number of Sequence Flows from Gateways), and

TNG (Total Number of Gateways of the model).

6 CONCLUSIONS AND

FURTHER WORK

This work shows the results obtained from carrying

out a family of experiments. This was done with the

objective of analyzing and evaluating the structural

complexity of business process models. The analysis

took place at a conceptual level of the models and

used a set of measures which were defined on the

basis of the BPMN standard notation.

As a result of this family of experiments we have

obtained a significant set of measures that could

serve as indicators towards the maintainability of the

business processes models expressed in BPMN.

From the 60 defined measures, 22 have been

correlated with BPMN model understandability or

modifiability in at least one of the experiments. With

these 22 measures, which have been validated as

results in order to measure the efficiency in the

accomplishment of the tasks (dependent variable), it

is possible to reject the formulated null hypotheses.

Future experimentation will focus upon

evaluating this set of measures which we consider

relevant from the results obtained in this first family

of experiments. To achieve this, new material will

be designed in which the validated metrics will be

grouped into three categories (participants and roles,

activities and control flows and decision nodes).

Specific material will be designed for each group.

This will allow us to obtain a higher variation of the

complexity in each subgroup of models which may

confirm the usefulness of the measures validated in

the first family. Other aspects to be tackled in the

future are:

• We shall conduct new experiments with the aim

of analyzing two further sub-characteristics of

the quality of the model, namely analyzability

and ease of learning, which are respectively

related to usability and maintainability.

• Furthermore, the development of business

process models within a company in the health

sector is being carried out, which will allow us to

use experimental models of real cases.

ENASE 2008 - International Conference on Evaluation of Novel Approaches to Software Engineering

ACKNOWLEDGEMENTS

This work has been partially financed by the

INGENIO Project (Junta de Comunidades de

Castilla-La Mancha, Consejería de Educación y

Ciencia, PAC08-0154-9262), MECENAS (Junta de

Comunidades de Castilla-La Mancha, Consejería de

Educación y Ciencia, PBI06-0024) and ESFINGE

Project (Ministerio de Educación y Ciencia,

Dirección General de Investigación/Fondos

Europeos de Desarrollo Regional (FEDER),

TIN2006-15175-C05-05.

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