Empirical and Theoretical Evaluation of USE and OCLE Tools

Carlos Vera-Mejia

, Maria Fernanda Granda

and Otto Parra

Department of Computer Science, Universidad de Cuenca, Av. 12 de Abril s/n, Cuenca, Ecuador

Keywords: Validation, UML, Class Diagram, USE, OCLE, Controlled Experiment.

Abstract: Validating the conceptual model (CM) is a key activity in ensuring software quality and saving costs,

especially when adopting any type of Model-driven Software Engineering methodology, in which standard

modelling languages such as UML and tools support for validation become essential. This paper analyses and

evaluates the main characteristics of the tools to support test-based validation in CMs. For this, two research

approaches were used: (1) an empirical evaluation to compare the effectiveness and fault detection efficiency

in a CM and analyses the level of ease of use of two tools used to validate requirements in UML conceptual

models, and (2) a complementary theoretical analysis. The study focuses on the class diagram, the most

common type of UML diagram, and two tools widely used by the modelling community for test-based

validation: USE and OCLE. Theoretical and empirical comparisons were carried out with the aim of selecting

an appropriate tool to validate UML-based CMs with OCLE achieving a better score.

1 INTRODUCTION

Testing is an essential part of the software

development life cycle (Ayabaca & Moscoso Bernal,

2017) as it allows the software engineer to find

defects, which can have a great impact on the

project’s budget. It is thus very important to detect

and eliminate them in the early stages of the

development process, for example, at the conceptual

model level, especially when Model-Driven Software

Engineering (MDSE) is used.

A conceptual model is an application that

designers want users to understand. There are many

ways to describe a conceptual model (Ayabaca &

Moscoso Bernal, 2017), such as: entity-relationship

diagrams, class diagrams based on the Unified

Modelling language (UML), process diagrams based

on Business Process Model and Notation -BPMN

(OMG, 2018), etc. On the language side, UML is the

de facto standard for modelling software systems.

UML (OMG, 2017) provides several diagrams to

model the structure of a software system, its

architecture and its behaviour.

Several commercial and open source tools to

support UML modelling are available, e.g. MagicDraw,

https://orcid.org/0000-0002-2029-2464

https://orcid.org/0000-0002-5125-8234

https://orcid.org/0000-0003-3004-1025

https://modeling-languages.com/uml-tools/

Papyrus, USE, OCLE among many others

. Although

each one has different characteristics, all of them offer

a graphic editor to assist in defining the UML models

and facilities to verify the finished model. However, we

believe that a theoretical and empirical comparison is

needed to help select a tool that can validate a UML-

based CM.

The study has two contributions to the current

literature: (1) a theoretical evaluation of two tools that

can be used to execute tests at the level of class

diagrams specified in UML and (2) an empirical and

comparative evaluation using TAM model

(Technology Acceptance Model) (Sauro & Lewis,

2012). We review the state of the art of UML class

diagram validation tools. Two of the most appropriate

tools are selected by analysing main characteristics,

advantages and disadvantages. An empirical and

comparative evaluation of the selected tools is

conducted to compare task oriented metrics such as

effectiveness, efficiency and ease of use of the tools

(Wetzlinger, Auinger, & Dörflinger, 2014).

This paper is organized as follows. Section 2

describes the background and the related work.

Section 3 describes the theoretical comparison of the

selected tools. Section 4 summarizes the experimental

plan of the empirical evaluation, as well as its main

246

Vera-Mejia, C., Granda, M. and Parra, O.

Empirical and Theoretical Evaluation of USE and OCLE Tools.

DOI: 10.5220/0010263102460253

In Proceedings of the 9th International Conference on Model-Driven Engineering and Software Development (MODELSWARD 2021), pages 246-253

ISBN: 978-989-758-487-9

results. Section 5 contains the final discussion of the

findings. Section 6 summarizes the threats to validity

and the conclusions and future work are summarized

in Section 7.

2 BACKGROUND AND RELATED

WORK

Software modelling is carried out by humans and so

is prone to defects being introduced in CMs, such as:

(1) missing, (2) inconsistent, (3) incorrect, (4)

redundant, and (5) ambiguous (Granda, 2015). The

number of defects should be minimised to reduce the

impact on software quality through different

verification and validation techniques, including

testing. Validation and Verification consists of

manipulating a CM under controlled conditions to:

(1) verify that it behaves as specified; (2) detect

defects and (3) validate user requirements

(Sommerville, 2011). As requirements validation is

difficult to judge solely by inspecting models, an

executable model is needed to evaluate the CM and

detect any misconceptions expressed in it.

Although, commercial programs are available as

they require a license they are outside the scope of

this work. We also found several tools to manage and

verify CM based on UML class diagrams (e.g., Dia

Lucidchart

, Magic Draw

, and Visio

, among others),

although they do not support the test-based validation

process, so were not considered in this work

According to (Myers, 2004), testing a program (in

our work, a CM) is trying to make it fail by injecting

defects into the software artefact. According to (Tort,

Olivé, & Sancho, 2011), the tests that can be

performed at the conceptual model level are related to

the verification of: (1) the consistency of a state, (2)

the inconsistency of a state, (3) the occurrence of a

domain event, (4) the non-occurrence of a domain

event, and (5) verifying the content of a state. For

example, to instantiate an object at the model level

and test whether it meets the constraints (i.e. pre-

conditions, post-conditions, and invariants) that are

expected as the objects change state, during the

execution of a test case. In this case, as there are other

failures to be analysed, a tool that allows the

execution of test cases to validate the conceptual

model should be used. This validation is very useful

during the software analysis and design phases, when

it is required to determine at an early stage whether

the CM meets the specified user requirements

www.dia-installer.de/

www.lucidchart.com/pages/

www.nomagic.com/products/magicdraw

www.products.office.com/en-in/visio/flowchart-software

(Ayabaca & Moscoso Bernal, 2017). In our study we

used Object Constraint Language (OCL) (OMG,

2013) to include the restrictions in a CM.

(Yu, France, Ray, & Lano, 2007) propose an

approach to automatically generate a sequence of

behavioural snapshots. The constraints on these

snapshot sequences are expressed by the OCL. In this

way modelling behaviour allows designers or

software engineers to use tools like USE

and OCLE

to analyse behaviour. This study helped us to identify

software that validates CM at the level of UML class

diagrams. Additionally, (Planas & Cabot, 2020),

analysed how modellers build UML models and how

good the modelling tools are to support the task of

obtaining a complete and correct CM based on a

definition of the requirements based on two

programs, one of which is commercial (i.e.

MagicDraw). (Bobkowska & Reszke, 2005) are

considering a set of six programs and focused on

finding the fastest modelling software and the

features that make it more efficient.

The present study adds a new perspective to

previous publications by carrying out an empirical

and comparative evaluation of tools that can be used

to detect defects through the execution of test scripts

on CMs using UML. For this, it focuses on the class

diagram, the most widely used UML diagram

(Dobing & Parsons, 2006), and other open source

modelling tools that automate the execution of test

scripts. To the best of our knowledge, the present

study is the first to analyse the effectiveness,

efficiency and ease of use of UML modelling

software from the point of view of test-based

validations of class diagrams.

3 THEORETICAL ANALYSIS

In this work, we selected USE (UML-based

Specification Environment) and OCLE (Object

Constraint Language Environment) because they

contain a language to write and execute test cases, are

open access, and still have support. Table 1 contains

a summary of the USE and OCLE characteristics.

These include: (a) general characteristics of their

operations; (b) validation options, associated with

their different methods of performing validation

processes; and (c) edition options, or the different

ways of editing a CM and its visualization. These

three groups of characteristics were defined by the

authors as a preliminary step to their evaluation.

http://www.db.informatik.uni-bremen.de/projects/ USE-

2.3.1/#overview

http://lci.cs.ubbcluj.ro/ocle/overview.htm

Empirical and Theoretical Evaluation of USE and OCLE Tools

247

Table 1: Summary of USE and OCLE characteristics.

For the theoretical comparison from the tester

perspective, relevant comparison criteria were

selected by convenience sampling (Etikan, Musa, &

Alkassim, 2016), and grouped as: (i) application

domain, (ii) types of verifications, (iii) types of

validations admitted, (iv) quality in terms of defect

types detected and validation objectives; and, (v) CM

validation environment (see Table 2). Both tools

support UML 2.0, which provides a more extensible

modelling language and allows CM validation and

execution. In USE, the CM is defined in USE code

and the restrictions in OCL and the two languages are

defined in the same file with extension “.use”.

In OCLE, the model is defined in a “.xml” file.

The restrictions are in a file with the extension “.ocl”.

Finally, there is a file with the extension “.oepr”

which contains the CM and the OCLs in a single

project. The relationships between the elements of a

CM that these tools support are based on UML

relationships (association, generalization, etc.).

Modelling at the metamodel level is only supported

by OCLE, and this modelling refers to the creation of

several CMs within of the same project. Both

programs can verify the data type entry both at the

level of attributes, parameters and returned values of

methods, as well as pre-conditions, post-conditions

and invariants. The difference is that USE restrictions

are entered in the project file using a separate text

editor, while in OCLE they can be entered or

modified by its graphic interface.

In the validation process, both USE and OCLE

perform validation techniques through CM tests and

CM simulation by using the object diagram. The CM

analysis in USE is automatic, since the CM is

validated when loading the project, while in OCLE

the CM is analysed after loading. All the classes,

relationships and restrictions are present, but the class

diagram is not displayed, and the diagram must be

created by dragging all the elements to a panel where

you can finally see how the diagram is designed.

Table 2: A technical comparison between selected tools.

We used the types proposed by Tort (Tort et al.,

2011) to analyse the types of test cases supported by

both tools, which were the same in each case. The

quality of CM validation types was analysed.

According to (Aladib, 2014), USE and OCLE detect

the same defects types: missing elements, bad

elements, unnecessary elements, and syntax when

entering data. Both programs aim to verify CM

consistency, correctness and completeness.

When executing the CM, USE presents a window

with the validation results executed, whereas OCLE

displays a drop-down menu of all the CM elements

showing all the validations in the form of messages,

while the elements are being selected from the menu.

For CM simulation feedback, both USE and OCLE

rely on simulating the CM by means of an object

diagram. They also allow batch test execution, i.e. a set

of validations can be executed in a single process. Both

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248

programs allow you to locate any defects within the

CM, OCLE indicates defects hierarchically (project

name - object - attribute name - incorrect value), while

USE locates defects directly by displaying the CM

element with a description. Both allow correction of

the elements until obtaining a validated CM.

Based on the analysis carried out, it can be said that

USE and OCLE have similar characteristics in the

selected comparison criteria, with few differences in

terms of functionality and characteristics: (a) each has

its own notation/language, (b) the structure of a USE

project contains a single file, while OCLE is made up

of several types of files, (c) the diagrams supported in

both tools, (d) how to insert the constraints in an CM,

(e) the level of automation that supports validation, (f)

the execution environment, (g) types of messages in

the feedback and, (h) feedback in the execution of the

constraints. The remaining analysed characteristics of

domain, verification, validation, quality and execution

are similar.

4 EMPIRICAL EVALUATION

This section describes the experimental plan, the

environment in which the experiment was carried out,

the procedure used and the data collection and analysis.

4.1 Experimental Planning

The experiment was designed according to (Wohlin

et al., 2012) and reported according to (Juristo &

Moreno, 2001). The goal definition is aligned with

the GQM (Goal/Question/Metric) paradigm (van

Solingen & Berghout, 1999).

4.1.1 Goal

To analyse USE and OCLE, for the purpose of

comparing them in a theoretically and empirically

way, with respect to their effectiveness and

efficiency in detecting faults in CMs and ease of use

when using these tools, from the viewpoint of

researcher, in the context of Computer Science

students interested in validating requirements.

4.1.2 Research Questions

 RQ

: Is there a significant difference between

the degree of USE and OCLE’s effectiveness in

detecting defects CMs?

 RQ

: Which of the two tools has a higher

degree of efficiency in detecting the greatest

number of defects in a CMs?

 RQ

: Is the perception of ease of use impacted

when the subjects are validating the CMs using

the selected tools?

4.1.3 Hypothesis

Three hypotheses were defined (see

Table 3

). The null

hypothesis (represented by the subscript 0) referred to

the absence of an impact of the independent variables

on the dependent variables. The alternative hypothesis

involved the existence of an impact and was the

expected result.

Table 3: Specification of hypothesis.

4.1.4 Variables and Metrics

a) Independent Variables: The validation tool was

considered as an independent variable (Juristo &

Moreno, 2001). This variable can have two values

(also known as treatments (Juristo & Moreno,

2001)): (i) users apply the USE tool, and (ii) users

apply the OCLE tool.

b) Dependent Variables and Metrics: The four

dependent variables (Juristo & Moreno, 2001) are:

• Effectiveness in Detecting Defects (Eldh,

Hansson, Punnekkat, Pettersson, & Sundmark,

2006). To investigate RQ

it was necessary to

measure the tools’ effectiveness in finding

defects. The result was a percentage that allowed

a comparative assessment, i.e., the lowest two

percentiles, were ineffective, while capacity

improved as it rose to 100%.

(1)

• Efficiency in Detecting Defects (Eldh et al.,

2006). This variable allowed RQ

to be

investigated and its metric was calculated

according to the following formula:

(2)

The result was a value that allowed the tools to be

evaluated comparatively, considering that the higher

values presented greater efficiency and the lower

values a deficiency (Eldh et al., 2006).

• Ease of Use. The degree to which a participant

considers a validation tool is effortless. This

variable responded to RQ3. To calculate this

metric, the TAM model was considered. We used

Empirical and Theoretical Evaluation of USE and OCLE Tools

249

a 7-point Likert scale to measure ease of use of the

tools, which is the arithmetic mean of this scale.

4.2 Experimental Context

4.2.1 Subjects

The experiment was carried on 18 subjects (2 women

and 16 men) between 18 and 22 years old, all

Computer Science’s students of the University of

Cuenca, with proven experience in conducting tests at

the code level, as well as in the use of modelling

methods and techniques, e.g. UML diagrams.

4.2.2 Conceptual Models

Four different conceptual models were used in our

study, two (CM A and CM B) for the training session,

and the other two (CM C and CM D) for the

experimental session with OCLE (

Figure 1

(a)) and

USE (

Figure 1

(b)). The CMs contain a variety of

features in a UML class diagram, including classes,

relationships, and different types of constraints.

Figure 1: Class diagrams of a) CM C and b) CM D.

4.2.3 Failures Injected into CMs

In this paper, we decide to consider five defects which

were injected into each CM used in the experimental

phase, as described in

Table 4

. These defects were

randomly selected from a list of defects previously

obtained from each model. According to a pilot study

which was carried out to validate the experimental

material, the expected average time to detect the five

defects is 3 minutes for each one.

4.3 Experimental Procedure

This section describes in detail the procedures used to

carry out the experiment.

Table 4: Defects in CM C and CM D.

Intensive training sessions were designed to

homogenize the knowledge and experience in the use

of the tools. Figure 2 summarizes the experimental

process, which was divided into the following sessions.

Figure 2: General scheme of the experiment.

4.3.1 Training Session

A general description of the tasks to be carried out

was made, after which the subjects filled out the

demographic questionnaire (ten minutes). Training in

the use of USE by CM A was then given. CM B was

used to train OCLE. During the 2-hour training

session the subjects solved some exercises and

received feedback on their performance.

4.3.2 Experimental Session

This session had an estimated duration of two hours

and was distributed as follows: (i) The subjects were

divided into two groups, the instructions on the

activities to be carried out were given (10 min) in a

document that included the description of each

proposed CM, and it allowed the information on the

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defects found by the subjects to be recorded

(description of the defect, defect found, defect

corrected, initial and final times). During the defect

detection they recorded the time it took to find each

defect using a digital clock. The subjects did not

know the number of defects injected into the CMs and

themselves decided when to end the defect analysis;

(ii) the first part of the experiment was carried out (30

min), the first group of subjects used USE, the second

group used OCLE and both used the CM C model

(see

Figure 1

(a)); (iii) a 20 minutes break; (iv) the

second part of the experiment was carried out (30

min) where the first group used OCLE and the second

group used USE with the CM D (see

Figure 1

(b)); (v)

the subjects answered the questionnaire on ease of use

for USE and OCLE (15 min); and, (vi) finally an

incentive was given to the subjects for their

participation in the experiment (15 min).

Additional information about the questionnaire

and the score of each participant, as well as the

injected and detected defects can be accessed on the

site http://t.ly/1mpu.

4.4 Analysis and Interpretation of

Result

This section summarizes the results of the evaluation.

Since RQ1 was to evaluate the tools’ effectiveness

in detecting defects, the number of defects found by

each in the different CMs was compared. Table 5

shows those defects detected (Column 2) by each

subject, and the Effectiveness value (Column 3). The

time spent and expected time data (columns 4 and 5)

were used to calculate the detection efficiency

(Column 6). The user satisfaction value is shown in

Column 7. Shapiro-Wilk tests, appropriate for <50

samples, were performed to assess the normality of

the samples.

4.4.1 Defect Detection Effectiveness

These variables did not follow a normal distribution

(<0.05) since the Sig. Values for the Shapiro-Wilk tests

were 0 for USE and 0.001 for OCLE. The data

obtained from both programs was considered in two

independent groups.

The Mann-Whitney U test was used to test our first

null hypothesis (H

). Figure 3 (a) shows the box plot

containing data on both programs’ effectiveness in

detecting defects and

Table 6

shows the results of the

Mann-Whitney U test.

It can be seen that the subjects’ effectiveness

differs according to the program used. In this case,

OCLE scored better than USE. We therefore did not

accept the null hypothesis H

. In other words, the

effectiveness is different for each tool, U=69.5, 2-

tailed p-value =0.001<0.05.

Table 5: Data collected by tool.

Figure 3: Box Plot for dependent variables per Tool.

Table 6: Values of the Mann-Whitney U Test for dependent

variables per Tool.

4.4.2 Defect Detection Efficiency

As in the previous analysis, all Sig. Values for the

Shapiro-Wilk tests were 0.0 for USE and 0.401 for

OCLE, which means that these variables did not have

a normal distribution (that is, <0.05). Considering

both types of data as independent groups, the non-

parametric Mann-Whitney U test was selected to

evaluate the second null hypothesis (H

). Figure 3 (b)

Empirical and Theoretical Evaluation of USE and OCLE Tools

251

shows the box plot containing data on the programs’

efficiency in detecting defects and Table 6 gives the

results of the Mann-Whitney U test. Given that the

detection efficiency is also affected by the type of

tool, OCLE performed better than USE (U = 69.000,

2-tailed p-value = 0.003< 0.05), making hypothesis

invalid.

4.4.3 Ease of Use

Our objective in RQ

was to detect any difference in

the ease of use score between USE and OCLE, for

which the overall score obtained from the 18 subjects

was compared (see

Table 5, Column 7). Figure 3 (c)

shows the box plot of the data collected for the ease

of use score for each tool. As the results show, the

score values gave a better result for OCLE than USE.

As in the previous analysis (RQ

and RQ

), the

Shapiro-Wilk test was performed on the collected

values. The Sig. Value was 0.218 for USE and 0.001

for OCLE, which means that this variable does not

have a normal distribution (<0.05). Considering both

groups of independent subjects, we selected the

Mann-Whitney U test (non-parametric test) to

evaluate the hypothesis H

From these data (see

Table 6), it can be concluded

that the OCLE ease of use score was statistically more

significant than that of the USE tool, which means

that we did not accept the null hypothesis H

and

concluded that the ease of use perceived by the

subjects was different for each tool; (U = 73.500, 2-

tailed p-value = 0.005 <0.05).

5 FINAL DISCUSSION

In this section, the findings of the empirical

experiment are discussed and interpreted according to

the research questions posed in Section 5.

From the empirical experiment, it was observed

that the effectiveness and efficiency show a

significant difference between both programs. This

difference is related to the mechanism and obstacles

that the subjects encountered when using both tools

(a little more in USE). The difference is due to the

programs’ defects reporting mechanism. USE detects

defects from the messages in its graphical and textual

interface, which takes a little more time, while OCLE

does so only in its graphic interface.

Some subjects highlighted that “the languages used

by the tools are intuitive and easy to use” and some

subjects indicated that "the information provided by

the tools was useful to distinguish the type of defect

and locate it in the CM". Through the analysis of the

TAM model, it was determined that OCLE presents

better results in terms of ease of use when used to

validate MCs. This could have been due to the different

facilities or the steps required for the process.

Therefore, from these results we can conclude that

OCLE is better than USE in defects detection in terms

of efficiency, efficacy and ease of use.

Regarding the practical implications and

applicability for model-driven practitioners, we

consider it important to emphasize the use of best

practices in conceptual modelling, such as those

detailed in (Kuzniarz & Staron, 2005). It should also

be remembered that some elements in a class diagram

must be created in a specific order even if only for

pragmatic reasons.

6 THREATS TO VALIDITY

Regarding internal validity, our threats are mainly

associated with the subjects and measurements. First,

the subjects in our experiment might have had

different prior knowledge of the tools before the

experiment (in the demographic questionnaire we

explicitly asked for this information and all claimed

they were unfamiliar with the tools), so we tried to

minimize this threat by training to homogenize their

knowledge and experience. Second, in order to

guarantee identical conditions for the experiment, all

the computers had the same operational conditions,

material, and MCs with equal complexity.

Regarding external validity, our threats are related

to the selection of modelling tools (OCLE and USE),

since they can have particular characteristics that

influence the time required for the detection of

defects. This threat was mitigated by selecting tools

with similar features and functionality. However, the

results of this experiment should not be generalized

to the modelling tools population were considered. In

future work we plan to replicate this study

incorporating commercial tools. Despite the fact that

the experiment was performed in an academic

context, the results could be representative with

regard to novice testers with no experience in CM

validations. With respect to the use of students as

experimental subjects, several authors suggest that

the results can be generalised to industrial

practitioners (Runeson, 2003).

Conclusion validity threats were mitigated by the

design of the experiment. We took a group of 18

students as a sample from the Systems Engineering

course. Furthermore, adequate tests were performed

to statistically reject the null hypothesis. The metrics

used allowed us to objectively evaluate the subjects’

effectiveness, efficiency and ease of use. As the

validity of the conclusion could be affected by the

number of observations, additional replicates with a

larger data set will be required to confirm or

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252

contradict the results obtained. The ease of use

questionnaire was designed using standard questions

and scales that have been shown to be highly reliable.

7 CONCLUSIONS AND FUTURE

WORK

Two research approaches were used to compare CM

validation tools based on UML class diagrams. First,

a theoretical analysis of the characteristics of these

tools was carried out using several criteria, i.e., they

had to be free license tools that support the creation

and execution of CM validation test cases. The

second research approach was to conduct an

empirical evaluation to compare the effectiveness,

efficiency and ease of use perceived by USE and

OCLE users.

The experimental evaluation reported notable

differences in the programs’ effectiveness, efficiency

and ease of use with OCLE achieving a better score.

As a future work, we plan to extend this study

considering other UML modelling tools (including

commercial tools) and also integrating other UML

diagrams to represent a conceptual model (e.g.,

activity, sequence), to see whether the results of this

study can be generalized. Finally, we intend to

implement a new validation tool that combines the

most outstanding functions of the different tools

analysed and solve any deficiencies found in them.

ACKNOWLEDGMENTS

This work has been supported by the Dirección de

Investigación de la Universidad de Cuenca (DIUC) –

Ecuador.

REFERENCES

Aladib, L. (2014). CASE STUDY Object Constraints

Language ( OCL ) Tools.

https://doi.org/10.13140/RG.2.1.4026.7927

Ayabaca, L. P., & Moscoso Bernal, S. (2017). Verificación y

Validación de Software. Killkana Técnica, 1(3), 25–32.

Bobkowska, A., & Reszke, K. (2005). Usability of UML

Modeling Tools. In Software engineering: evolution

and emerging technologies (Vol. 130, pp. 75–86).

Netherlands.

Dobing, B., & Parsons, J. (2006). How UML is used.

Communications of the ACM, 49(5), 109–113.

https://doi.org/10.1145/1125944.1125949

Eldh, S., Hansson, H., Punnekkat, S., Pettersson, A., &

Sundmark, D. (2006). A framework for comparing

efficiency, effectiveness and applicability of software

testing techniques. In Testing: Academic & Industrial

Conference - Practice And Research Techniques (TAIC

PART’06) (pp. 159–170). Windsor.

Etikan, I., Musa, S. A., & Alkassim, R. S. (2016).

Comparison of Convenience Sampling and Purposive

Sampling. American Journal of Theoretical and

Applied Statistics, 5(1).

Granda, M. F. (2015). What do we know about the Defect

Types detected in Conceptual Models ? In IEEE 9th Int.

Conference on Research Challenges in Information

Science (RCIS) (pp. 96–107). Athens, Greece.

Juristo, N., & Moreno, A. M. (2001). Basics of Software

Engineering Experimentation.

Kuzniarz, L., & Staron, M. (2005). Best Practices for

Teaching UML Based Software. In MoDELS 2005

Workshops (pp. 320–332).

Myers, G. J. (2004). The Art of Software Testing. New

Jersey, USA: John Wiley and Sons.

OMG. (2013). Object Constraint Language (OCL).

https://doi.org/10.1145/1921532.1921543

OMG. (2017). Unified Modeling Language. Retrieved from

https://www.omg.org/spec/UML/About-UML/

OMG. (2018). Business Process Model and Notation

BPMN. Retrieved from

https://www.omg.org/spec/BPMN/2.0/About-BPMN/

Planas, E., & Cabot, J. (2020). How are UML class

diagrams built in practice? A usability study of two

UML tools: Magicdraw and Papyrus. Computer

Standards & Interfaces, 67(October 2018), 103363.

Runeson, P. (2003). Using students as experiment subjects–

an analysis on graduate and freshmen student data. In

7th International Conference on Empirical Assessment

& Evaluation in Software Engineering (pp. 95–102).

Sauro, J., & Lewis, J. R. (2012). Quantifying the User

Experience: Practical Statistics for User Research.

Sommerville, I. (2011). Software Engineering. In M.

Horton (Ed.), Software Engineering (9th ed., pp. 41–

42). Boston Columbus.

Tort, A., Olivé, A., & Sancho, M.-R. (2011). An approach

to test-driven development of conceptual schemas.

Data & Knowledge Engineering, 70(12), 1088–1111.

Van Solingen, R., & Berghout, E. (1999). The

Goal/Question/Metric Method-A Practical Guide for

Quality Improvement of Software Development.

McGraw-Hill.

Wetzlinger, W., Auinger, A., & Dörflinger, M. (2014).

Comparing effectiveness, efficiency, ease of use, usability

and user experience when using tablets and laptops. In

International Conference of Design, User Experience, and

Usability (Vol. 8517 LNCS, pp. 402–412).

Wohlin, C., Runeson, P., Höst, M., Ohlsson, M. C., Regnell,

B., & Wesslén, A. (2012). Experimentation in software

engineering. Springer-Verlag Berlin Heidelberg.

Yu, L., France, R. B., Ray, I., & Lano, K. (2007). A light-

weight static approach to analyzing UML behavioral

properties. In 12th IEEE International Conference on

Engineering of Complex Computer Systems (pp. 56–

63). Auckland.

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