Entity-relationship Modeling Tools and DSLs: Is It Still Possible to
Advance the State of the Art from Observations in Practice?
Jonnathan Lopes
, Maicon Bernardino
, Fábio Basso
and Elder Rodrigues
Postgraduate Program in Software Engineering (PPGES),
Laboratory of Empirical Studies in Software Engineering (LESSE),
Federal University of Pampa (UNIPAMPA), Av. Tiarajú, 810, Ibirapuitã, CEP 97546-550, Alegrete, RS, Brazil
Multivocal Literature Mapping, Domain Specific Language, Conceptual Data Model, Conceptual Model,
Database Modeling.
The variety of database system technologies that became available in recent years makes difficult the selection
of tools for modeling entity-relationship (ER). The published mapping studies on this topic date back to 2000,
thus outdated and limited to guide designers towards the recent innovations selection for the design and im-
plementation of databases. In this sense, we contribute with an overview of the recent innovations through a
systematic literature mapping complemented by research in the gray literature. This paper scopes ten (10) pri-
mary studies focused on Domain-Specific Languages (DSL) and identifies fifty-five (55) tools already applied
in industry and academia for ER modeling at the conceptual, logical, and physical level. Hence, as a signif-
icant increment to existing mapping studies, this presents the state-of-the-art and practice for ER modeling,
including its characterization and research gaps.
In the software development process, data models can
undergo different transformations. The three-level
modeling approach to database (DB) design (ANSI,
1975) suggests separating data models into different
degrees of abstraction, known as conceptual, logical,
and physical models. The conceptual modeling of
database systems requires the highest level of abstrac-
tion, and the most widespread technique for its devel-
opment is the conceptual data modeling proposal con-
ceived by Chen (1976). This proposal, influenced by
the three-level approach, was so well accepted that it
was considered a definitive reference for DB model-
ing (Cougo, 2013).
DB modeling is a relevant part of the software de-
velopment lifecycle, which generally some Domain-
Specific Language (DSL) supported (Voelter, 2009).
According to Van Deursen et al. (2000), a DSL is
an executable programming language or specifica-
tion language that offers expressive power focused,
through appropriate notations and abstractions, usu-
ally restricted to a domain-specific problem. Like
other languages, a DSL must present a set of sen-
tences well defined by syntax and semantics them-
selves. For Fowler (2010), a DSL is a computer pro-
gramming language with limited expressiveness and
focused on a particular domain. In addition to code-
first approaches, the adoption of DSLs in software
processes promotes a significant increase in produc-
tivity, quality, ease of use, and flexibility (Mernik
et al., 2005; Vara et al., 2014). The great advantage
of using DSLs is an abstraction, which takes a less
complex level of knowledge needed for their devel-
opment. In this way, domain experts can understand,
validate and modify the code through model transfor-
mations and refinements (Van Deursen et al., 2000).
According to Mernik et al. (2005), we can clas-
sify DSLs into three dimensions: origin (internal or
external), appearance (textual, graphical, tabular, or
symbolic), and implementation (run mode). In gen-
eral, the principal consideration for creating a DSL
should be where it comes from, as each approach
has specific advantages and disadvantages (Fowler,
2010). On the one hand, we can design an inter-
nal DSL around the grammatical rules of an existing
language, which can be a General Purpose Language
(GPL) or another DSL. On the other hand, an exter-
Lopes, J., Bernardino, M., Basso, F. and Rodrigues, E.
Entity-relationship Modeling Tools and DSLs: Is It Still Possible to Advance the State of the Art from Observations in Practice?.
DOI: 10.5220/0011044500003179
In Proceedings of the 24th International Conference on Enterprise Information Systems (ICEIS 2022) - Volume 1, pages 179-186
ISBN: 978-989-758-569-2; ISSN: 2184-4992
2022 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
nal DSL is a language with syntax owned and depends
on its infrastructure for lexical, syntactic, and seman-
tic analysis, interpretation, compilation, optimization,
and code generation (Mernik et al., 2005). As for ex-
ecution, conceptually, this dimension ranges from the
specification of graphic and textual notations of lan-
guages applied on specific domains to implemented
The objective of this study is to map existing
DSLs to DB modeling at any of the three model lev-
els. To meet this purpose, we carried out a Multivocal
Literature Mapping (MLM) to find primary studies
and tools that present approaches, methods, or tech-
niques for the DB models creation.
We organized this study as follows. Section 2
presents the discussion of related works. The proto-
col used we detailed in Section 3. Its execution is
described in Section 4, while the obtained results are
analyzed and discussed in Section 5. Section 6 de-
scribes the threats to the study’s validity. Section 7
points out possible gaps for future research and, fi-
nally, Section 8 presents the final remarks.
Before performing this study, we performed an ex-
ploratory ad hoc
search in the literature for studies
that proposed research related to approaches to mod-
eling entity-relationship (ER) in DB, with a focus on
primary studies and mappings.
Gregersen and Jensen (1999) performed a survey
and analyzed the design properties applied to spec-
ify temporal attributes in ER modeling approaches.
The aiming study presents the new concepts used to
facilitate temporal modeling in 19 different propos-
als. These proposals are based on models, namely:
ER, EER, OO, and ERC+. As a result, the differ-
ent mapping models for relational models are high-
lighted, used in the database implementation plat-
form, which has algorithms that optimize the defi-
nition of attributes with temporal value. In addition,
we evaluated ten (10) models against various design
properties to verify their completeness. The authors
conclude that while the models collectively have ade-
quate design coverage, none satisfy all aspects of the
evaluated modeling. It denotes that the temporal as-
pects modeling in data models is a motivating activity
and a promising research area.
Siau (2012) conducted a literature review on re-
lational and semantic approaches to ER modeling.
Ad hoc search is a customized search, using a tailor-
made method, and structured to meet specific needs without
necessarily a high degree of scientific rigor.
He shows that most studies point out that semantic
models are better than relational models and that, to
a lesser degree, other studies do just the opposite.
Early studies argue that, in general, semantic mod-
els are easier to use for end-users as they offer no-
tations with a higher level of abstraction for domain
modeling. More recent studies that oppose the first
observation, on the other hand, commonly assert that
the concepts of relationships between entities in re-
lational models are more understandable in making
their relationship identifiers explicit. In addition, it
discusses some works that claim there are no signifi-
cant differences between the two approaches studied,
citing, for example, the author carried out a controlled
experiment using the same SQL statement, i.e. the
subjects did not show any difference significant when
they performed their modeling using the different ap-
We can mention three other relevant non-
systematic mappings of ER approaches. The un-
structured literature review presented in Ma and Yan
(2010) searches contributions to uncertain informa-
tion in conceptual modeling, encompassing complex
objects and inaccurate data. In Lorenz et al. (2016),
the authors present an unstructured mapping litera-
ture study comprehending industry and academic best
practices in ER projects.
In Torres et al. (2017), the authors describe a map-
ping study to characterize nine (9) platforms that sup-
port Object-Relational Mapping Solutions (ORMS)
that includes examples in UML and Barker notations.
These are complementary studies and do not overlap
with the research questions proposed in this research.
Furthermore, unlike ours, the studies aforementioned
are awkward to replicate due to the lack of a protocol.
A Multivocal Literature Mapping (MLM) is a form of
Systematic Literature Mapping (SLM) that includes
gray literature. MLMs are helpful for researchers and
practitioners as they provide comprehensive insights
into the state-of-the-art and practice in a particular
area. In this study, we conducted an MLM
using the
SLM process defined by Petersen et al. (2008) and the
guidelines proposed by Garousi, V. and Felderer, M.
and Mäntylä (2019) to search the gray literature.
To this end, we defined the following Research
Questions (RQs): RQ1. What is the state-of-the-
Data from the experimental package available in the
Zenodo DOI repository: 10.5281/zenodo.5724135
ICEIS 2022 - 24th International Conference on Enterprise Information Systems
art in developing DSLs for transforming ER models?
e.g. modeling or manipulation, and transformation.
RQ1.1. What are refinement methodologies, tech-
niques, and proposals (automated design) based on
data models presented in the primary studies? e.g.
normalization. RQ1.2. What tools support the devel-
opment of these DSLs? RQ2 What evaluation meth-
ods are used in primary studies? RQ2.1. What are
the positive and negative points observed in carrying
out the studies? RQ2.2. What challenges do the pri-
mary studies point out? RQ3. What are the tools for
conceptual modeling of databases? RQ3.1. What no-
tations or languages do these tools use? RQ3.2. What
modeling levels (Conceptual, Logical, Physical) do
these tools support?
Some of the RQs (RQ1, ..., RQ2.2) were essen-
tially answered through academic studies, thus seek-
ing to understand the state-of-the-art, while others
(RQ3, ..., RQ3.2) reflected the investigation of the
state-of-the-practice and therefore, we used gray lit-
erature for a better conclusion.
For the SLM, it was necessary to access Digi-
tal Libraries (DLs) to obtain scientific studies. For
the research conducted, the DLs consulted should in-
clude three mandatory requirements: have a web-
based search engine; have a keyword search engine,
and; cover primary studies in the field of Computer
Science. Therefore, we carried out the search in five
DLs: (i) ACM DL; (ii) IEEE Xplore; (iii) ScienceDi-
rect; (iv) Scopus; (v) SpringerLink. Afterward, we
established the most expressive terms and synonyms
for the research object. The combination generated
the generic string in Figure 1.
(DSL OR Domain Specific Language OR Domain-Specific Language OR
Domain-Specific-Language OR DSML OR Domain Specific Modeling
Language OR Domain-Specific Modeling Language OR
Domain-Specific-Modeling-Language OR Query Language) AND (ER OR
Entity-Relationship OR Enhanced Entity-Relationship OR Extended
Entity-Relationship OR Database)
Figure 1: Search string.
We elaborated selection criteria for primary stud-
ies to include the most relevant works. We divided the
criteria into:
(i) Inclusion Criteria (IC): (IC1.) The study pro-
poses some technique, method, approach or tool for
the representation and transformation of DB mod-
els using DSL. (ii) Exclusion Criteria (EC): (EC1.)
Study with less than four pages; (EC2.) Study not
written in English; (EC3.) Duplicate study; (EC4.)
Study not provide full access to its content; (EC5.)
Study not meet IC1.
In addition, we established a set of seven (7) qual-
ity assessment criteria (QCs). The purpose of the
quality assessment was both to qualify the relevance
of the studies and assist in the interpretation of the
results, as well as to limit the SLM bias (Dybå and
Dingsøyr, 2008). In addition, we gave a score to the
QCs: (i) T:Total, fully considering the evaluated QC;
(ii) P:Partial, partially considering the evaluated QC
and according to its weight; (iii) N:Negative, does not
include the evaluated QC at all.
The maximum possible score, evaluating all cri-
teria, was ten (10.0) and the minimum zero (0). Each
QC had a specific weight (1 1.5 2) depending on
its importance considered for this study. We consid-
ered aspects relevant to the research these QCs based
on, namely report (QC1, QC4, QC5), rigor (QC2,
QC3), credibility (QC2, QC3 ) and relevance (QC1,
QC6, QC7). Table 1 presents the list of QCs and their
respective weights.
Table 1: Quality Assessment Criteria
QC Description Weight
QC1 Does the study make any contribution to the field of DB mod-
QC2 Does the study present techniques or proposals for refine-
ment based on data models ?
QC3 Does the study apply any form of empirical evaluation? 1.5
QC4 Does the study describe the characteristics of the DSL cre-
ation process?
QC5 Does the study characterize the model transformation activi-
ties for different DB technologies?
CQ6 Does the study have positive and negative points observed in
its execution?
QC7 Does the study point out challenges arising from its imple-
Once we carry out the quality assessment, it is
necessary to perform the data extraction step to in-
terpret the results. Thus, a data extraction form was
created, consisting of a series of items, such as the
origin of the solution, the year of publication, the ob-
jective, the evaluation of the proposal, the positive and
negative points observed, among other data.
We used the Google search engine to seek
database modeling tools in gray literature. For this,
we defined combinations of keywords from the search
string and adapted them to the context of the search on
the web: (i) ERD Modelling Tool; (ii) ERD Design
Tool; (iii) Conceptual Design of Database; (iv) Con-
ceptual Modelling of Database; (v) Conceptual Mod-
elling; (vi) Database Modelling Tool; (vii) Database
Design Tool. The verification of results was limited
to 10 (ten) pages in the search engine for each key-
The acceptance of tools into MLM was subject
to a selection process based on certain requirements.
First, the tool should provide access for some form of
use (e.g. trial version), it would need to support some
level of DB modeling and it would need to have inter-
face in English or Portuguese (our native language).
Afterwards, the included tools should have the nota-
tions and modeling levels extracted, along with other
relevant information, and then categorized.
Entity-relationship Modeling Tools and DSLs: Is It Still Possible to Advance the State of the Art from Observations in Practice?
For the execution of the SLM, it was necessary to
adapt the syntax of the generic string to generate other
versions, seeking to adapt the peculiarities of param-
eterization of the different databases used. Then, we
performed a search for studies in the databases. We
obtained the number of primary studies returned by
the digital library as follows: (i) IEEE Xplore: 513;
(ii) Scopus: 465; (iii) ACM: 1240; (iv) Springer Link:
683; (v) ScienceDirect: 826.
With the initial set of 3727 primary studies identi-
fied, we defined five (5) selection cycles. In these it-
erations, duplicate studies were excluded (with 3513
remainings), selection of studies based on title and ab-
stract (with 34 remainings), selection based on full
text (with 18 remainings), and selection based on
quality assessment (with 10 remainings). Each itera-
tion had the objective of eliminating studies that were
outside the scope of the research or considered not rel-
evant. In the last iteration, 18 studies had their quality
analyzed. We established that only studies with scores
above five (5) would be accepted.
Thus, after applying the QCs, eight (8) studies
were excluded. The final set of ten (10) studies ap-
proved by the SLM proceeded to the data extraction
stage. With academic works, the lowest score was
5.3, while the highest reached ten (10). Table 2 sum-
marizes the results obtained in the quality assessment.
Table 2: Quality Assessment Results.
Primary Studies Quality Criteria
Ayadi et al. (2016) P P T P N T P 5.7
Celikovic et al. (2014) T T N T P P T 7.0
Dimitrieski et al. (2015) T T T T T T T 10.0
Hammer and Mc Leod (1981) T T N T N P P 5.5
Jagannathan et al. (1988) T T N T P N N 5.5
Kersten et al. (2011) P T T P N N P 5.3
Litwin et al. (1989) P T N T N P T 5.4
Mazairac and Beetz (2013) T T P T N N P 5.9
Shipman (1981) T T N T P N P 6.0
Tian et al. (2006) T P N T P P T 6.4
Caption: T = Total, P = Partial, N = Negative
The execution of the search protocol in the gray
literature returned a total of 132 tools. After sum-
marizing, duplicates were excluded (with 67 remain-
ings). During this process, we did not evaluate twelve
(12) tools due to some unavailability type, e.g. the im-
possibility of installation or incompatibility with the
environment used. In the end, it was possible to run
usage tests on 55 tools, and consequently, extract rel-
evant data for the study. During the use of the tools,
we created simple database models, in which we tried
to observe the support for modeling levels and the no-
tations and languages used.
Regarding the state-of-the-art (RQ1) in the develop-
ment of DSLs applied to database modeling, a study
was identified that presents the Multi-paradigm Infor-
mation System Modeling Tool (MIST) (Dimitrieski
et al., 2015) , which uses a bidirectional DSL called
EERDSL, for conceptual modeling using the Ex-
tended Entity-Relationship (EER) approach. Bidirec-
tional modeling and transformation is a model-driven
engineering paradigm concept. In this context, we
are referring to a DSL capable of transforming mod-
els to code, and from code to models. As for the
refinement methodologies, techniques, or proposals
(RQ1.1) based on data models, only the two stud-
ies mentioned applying concepts for refinement, sup-
ported by database normalization to help developers
use their solutions.
With regard to the technologies used to support
DSL development (RQ1.2), we registered the fol-
lowing: Xtext, Xtend, Sirius and Eugenia (Celikovic
et al., 2014; Dimitrieski et al., 2015), StarUML
(Ayadi et al., 2016), IfcDoc Tool and ViewEdit Tool
(Mazairac and Beetz, 2013), MonetDB (Kersten et al.,
2011), Java, JFlex, and JCup (Tian et al., 2006). How-
ever, older studies were generally DSL specifications,
not showing any form of implementation or tool used
(Jagannathan et al., 1988; Litwin et al., 1989; Ship-
man, 1981).
The database representations adopted (RQ1.3)
have Tables and Functions in all primary studies
analyzed. There are also explicit references to the
definition of Stored Procedures, Triggers, and
Views in other studies. Table 3 summarizes the data
retrieved from each of the primary studies.
Table 3: Represented Database Objects.
Primary Studies DB Objects
Reference DSL TB SP F T V
Ayadi et al. (2016) Ayadi’s Notation X
Celikovic et al. (2014) EERDSL v.1 X X X
Dimitrieski et al. (2015) ERRDSL v.2 X X X X X
Hammer and Mc Leod (1981) SDM X X
Jagannathan et al. (1988) SDM X X
Kersten et al. (2011) SciSQL X X
Litwin et al. (1989) MSQL X X X X X
Mazairac and Beetz (2013) BIMQL X X X
Shipman (1981) DAPLEX X X
Tian et al. (2006) NeuroQL X X
Caption: TB = Tables, SP = Stored Procedures, F = Funtions, T = Triggers, V = Views
On the methods used to evaluate DSL (RQ2),
there is only one preliminary study that presents the
validation of the proposal (Dimitrieski et al., 2015)
using 16 participants, two (2) experts in IHC, three
ICEIS 2022 - 24th International Conference on Enterprise Information Systems
(3) experts in systems modeling and 11 students (6
masters in the database area and five (5) doctoral stu-
dents with experience in modeling). In general, the
other studies indicate the lack of an evaluation of their
propositions as possible future work.
Among the positive and negative aspects observed
(RQ2.1), the positive aspects of being easy to under-
stand and with intuitive modeling and platform in-
dependence (Mazairac and Beetz, 2013; Tian et al.,
2006) stand out. The negatives were the lack of auto-
matic SQL code generation (Ayadi et al., 2016; Dim-
itrieski et al., 2015) and the lack of actual implemen-
tation of DSLs, with only specifications among the
works (Ayadi et al., 2016; Hammer and Mc Leod,
1981; Jagannathan et al., 1988; Kersten et al., 2011;
Tian et al., 2006).
In general, the main challenges identified by the
studies (RQ2.2) are the evaluations of the approaches,
as well as the evolution and/or simplification of the
proposals. Table 4 presents the DSLs concerning their
type. However, it is worth noting that the studies that
mark the column bidirectional (Celikovic et al.,
2014; Dimitrieski et al., 2015) are different versions
of the same DSL implementation, while (Hammer
and Mc Leod, 1981; Jagannathan et al., 1988) are a
specification and implementation of DSL based on
this specification, respectively.
Table 4: Categorization of DSLs Proposals.
Primary Studies DSL Type
Reference DSL T G B
Ayadi et al. (2016) Ayadi’s Notation X
Celikovic et al. (2014) EERDSL v.1 X
Dimitrieski et al. (2015) EERDSL v.2 X
Hammer and Mc Leod (1981) SDM X
Jagannathan et al. (1988) SDM X
Kersten et al. (2011) SciQL X
Litwin et al. (1989) MSQL X
Mazairac and Beetz (2013) BIMQL X
Shipman (1981) DAPLEX X
Tian et al. (2006) NeuroQL X
Caption: T = Textual, G = Graphical, B = Bidirectional.
As for the state of practice (RQ3) of the tools used
in the modeling of DBs, we mapped 55 tools. Thus,
there was the classification as to its type, being 29 ex-
clusive modeling DBs (Data Modeling), 13 of model-
ing with connection to integrated DBs and execution
of queries (Full IDE), 10 with support for diagram-
ming of different types of models (Diagramming) and
three (3) tools designed for large companies (Enter-
prise Modeling).
Regarding the notations used in the tools (RQ3.1),
we identified 18 notations, where the Crow’s Foot no-
tation highlights 35 occurrences and the IDEF1X no-
tation with 23 records. Table 5 listed these and other
data. And finally, concerning the models supported by
the tools (RQ3.2) it was found that, individually, 26
tools support conceptual modeling, 48 tools support
logical modeling and 37 support physical modeling.
Figure 2 presents the set representing the intersections
of the model support and the tools is presented in the
Venn diagram. The numbers inside the diagram rep-
resent the tool IDs, also displayed in Table 5.
7, 21, 41
10, 11, 39
15, 45, 50
1, 3, 4,
8, 9, 16, 18, 26,
31, 40, 53, 55
5, 12, 20, 23,
33, 34, 35
37, 38, 51
2, 13, 14, 17, 19,
22, 24, 25, 27, 28,
29, 30, 32, 36, 42,
43, 44, 46, 47, 48,
49, 52, 54
Figure 2: Venn diagram of the models supported in the
We identified threats to the study result in the MLM
performed and then categorized it into the following
types: construct validity, internal validity, external va-
lidity, and conclusion validity, according to the guide-
lines of Cook and Campbell (1979); Wohlin et al.
Construct Validity: Addresses the possibility that
the RQs or search terms that structure the search
string are inappropriate or incomplete. To mitigate
these threats, we consulted DSL and researchers of
data modeling. In addition, we perform a pilot survey
to assess the consistency of our search string. An-
other threat is the quality of published material that
has been collected in gray literature.
Internal Validity: Some possible threats are the im-
plementation of incorrect search methods, which can
lead to the exclusion of relevant studies, an applica-
tion of poor data extraction strategy, the occurrence
of biases in the selection or content of primary stud-
ies. In an attempt to mitigate these risks, we defined a
protocol based on well-established reference models
Entity-relationship Modeling Tools and DSLs: Is It Still Possible to Advance the State of the Art from Observations in Practice?
Table 5: Database Modeling Tools.
Type Models Supported Notations Plataform License
1 AnalyseSI X X X X X X X
2 Aqua Data Studio ER Modeler X X X X X X X
3 Astah X X X X X X X
4 brModelo X X X X X X X
5 Creately X X X X X X
6 Database Deployment Manager X X X X X X X
7 Database Workbench X X X X X X
8 DB Designer X X X X X
9 DB-Main X X X X X X
10 DBDesigner 4 X X X X X X X X
11 DBDesigner.net X X X X X
12 dbdiagram.io X X X X X
13 dbDiffo X X X X X X X X
14 dbForge Studio for MySQL X X X X X X X
15 DBSchema X X X X X X X X
16 DBVisualizer X X X X X
17 DbWrench X X X X X X
18 DeZign for Databases X X X X X X X
19 Dia X X X X X X X X X X X
20 dModelAid X X X X X
21 Enterprise Architect X X X X X X X X
22 ER-Assistant X X X X X
23 ER/Builder X X X X X X
24 ER/Studio Data Architect X X X X X X X X
25 ERD Concepts X X X X X X X
26 ERDesigner NG X X X X X X
27 ERDPlus X X X X X X
28 Erwin Data Modeler X X X X X X X
29 GenMyModel RDS X X X X X X
30 InfoSphere Data Architect X X X X X X X
31 Jeddict X X X X X X
32 ModelRight X X X X X X
33 MySQL Workbench X X X X X X X X X
34 N Data Modeler X X X X X X X X
35 Navicat Data Modeler X X X X X X X X X
36 Open ModelSphere X X X X X X X X X
37 Oracle SQL Developer Data Modeler X X X X X X X
38 pgModeler X X X X X X X
39 PowerDesigner X X X X X X X X X X X
40 QuickDBD X X X X X
42 Software Ideas Modeler X X X X X X X
43 SQL Database Modeler X X X X X X
44 SQL Maestro X X X X X X
45 SQL Power Architect X X X X X X
46 SQL Server Management Studio X X X X X
48 SQLyog X X X X X X
49 Toad Data Modeler X X X X X X X
50 Valentina Studio X X X X X X
51 Vertabelo X X X X X
52 Visual Paradigm X X X X X X X
53 Win A&D X X X X X X
54 WWW SQL Designer X X X X X X
55 xCase X X X X X X
Caption: DM (Data Modeling), FIDE (Full IDE), DG (Diagramming), EM (Enterprise Modeling) | C (Conceptual), L (Logical) F (Physical) | CF (Crow’s Foot), CN (Chen’s Notation),
MN (Merise Notation), BN (Barker’s Notation), AN (Arrow Notation), ON (Other Notation) | D (Desktop), W (Web), C (Comercial), FC (Free of Charge)
ICEIS 2022 - 24th International Conference on Enterprise Information Systems
in the literature.
External Validity: External threats often address
whether findings from one study can be generalized to
another domain. One reason for this threat would be
the occurrence of primary studies selection containing
incomplete information. However, it is likely that, as
this is an area of intersection between data modeling
and DSLs, the results cannot be generalized to other
research topics, thus reducing this risk naturally.
Conclusion Validity: One possible threat is bias in
data extraction, which leads to completion errors. To
alleviate this problem, we performed a careful read-
ing and tests of the use of the tools, and there was
the synthesis of data in electronic spreadsheets for a
better analysis.
This section presents the main research perspectives
on still open questions that researchers can explore in
the future.
Lack of Previous Research on the Topic: We easily
found in the literature different secondary studies of
domains in data modeling or DSLs. Notwithstanding,
before the execution of this MLM, we carried out an
ad hoc search for other research initiatives similar to
ours, but no results with exactly the same objectives
were obtained.
Absence or Limitation in the Evaluation of Study
Proposals: There is a consensus that software tech-
nologies need to be investigated and empirically eval-
uated and that primary studies should generally re-
port how their proposal is validated. However, the
lack of evaluation descriptions in the studies seen in
this MLM was frequent during the analysis studies.
It demonstrates a weakness in the methodology im-
plemented to carry out the studies that highlight the
great concern with development. It is also possible
to understand that activities inherent to Software En-
gineering that could add value to the quality of the
study proposal are also not discussed, including do-
main analysis for the creation of DSLs or their main-
Automatic SQL Code Generation in Academic
Studies: The lack of emphasis on approaches to
transform data models is notable as opposed to tools
applied in the industry, although at least two stud-
ies deal with automatic SQL code generation for
databases. Although most of the identified DSLs
have good specifications, we did not find initiatives
to model databases using textual DSLs. This fact may
indicate the odds this potential field to become a rele-
vant research area.
Every year, several contributions to modeling (ER)
are published. Database modeling is a fundamental
area in Software Engineering, and DSLs that support
this activity are did not trivially find in the literature.
Alternatives research for database modeling is nec-
essary to follow the evolution and trends of various
Database Management Systems (DBMSs). This pa-
per aims to provide an overview of the DSLs used
by ER modeling and the tools used in the industry
through an MLM. The principal motivation for this
research is that we believe that the usability of DSLs,
with their high power of domain expertise, is an el-
ement that can sharp help in modeling activities in
DBMSs, especially at a conceptual level.
This study covered 3513 distinct studies with the
intention of investigating primary studies that pro-
posed DSLs for modeling DBs. Likewise, we raised
a set of 67 different tools that support database mod-
eling, seeking to map their notations and supported
models. We detailed the MLM protocol, its conduc-
tion and subsequent, analysis of the results obtained.
In the end, we selected ten (10) primary studies and
55 tools to be analyzed quantitatively and qualita-
tively. As a result, only DSLs currently used to sup-
port ER modeling and tools used in database design
were rated. Among the results, the study by Dim-
itrieski et al. (2015) stands out, which presents a bidi-
rectional modeling tool that applies its DSL based on
the EER approach.
Finally, our research provides shreds of evidence
that each year a significant of papers presenting dif-
ferent notations types are published, as seen in the
number of publications analyzed from the result re-
turned from the base string. It is somewhat surpris-
ing, given that the ER notations used today by indus-
try and academia, such as those by Peter Chen and
Barker, are not recent proposals. Therefore, we con-
cluded that ER modeling remains a wide field of re-
search with a few gaps, e.g. the loss of tools that sup-
port the design in many lifecycles through automated
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