Overcoming Obstacles in Model-Driven Engineering: Lessons from the

Software Industry

Sayeda Rahnuma Akthar

1 a

, Muhammad Rezaul Islam

2 b

, Marzan Binte Hasan

2 c

Mahpara Sayema Siddiqua

2 d

, Shadat Irtisamul Haque

2 e

, Jamil Ahmad Saad

2 f

Farzana Sadia

3 g

and Mahady Hasan

2 h

Department of Computer Science, Independent University Bangladesh, Dhaka, Bangladesh

Department of Computer Science and Engineering, Independent University Bangladesh, Dhaka, Bangladesh

Department of Electronic Engineering & Technology, Universiti Malaysia Perlis (UniMAP), Perlis, Malaysia

Keywords:

Software Modelling, System Modelling, Model-Driven Engineering (MDE), Developers, Practitioners.

Abstract:

Software modeling, as used in Model-Driven Engineering (MDE), is the process of abstracting software sys-

tems using formal or informal notations to help with communication, analysis, and design. This study looks

into the difﬁculties Bangladesh’s software industry faces while using model-based engineering, or system

modeling. A survey of several companies in Bangladesh was carried out to get opinions from professionals

such as software developers, project managers, test engineers, and architects. The data gathering instrument

utilized was Google Forms, and the questionnaire’s design was informed by extant literature. Results show

that about 75.1% of respondents use system modeling in their projects, and 56.3% of them say it helps to

streamline project development. Strong internal consistency among survey items pertaining to system model-

ing methodologies is indicated by a high Cronbach’s alpha value (0.93007), which suggests growing adoption

among software enterprises in Bangladesh. Response analysis revealed trends and patterns that were repre-

sented through data quantiﬁcation. Based on these results, the paper offers suggestions for resolving these

problems and advancing more comprehensive system modeling methodologies. The steps that have been pro-

posed to address the challenges in Model-Driven Engineering (MDE) include conducting in-depth research

to determine the underlying causes of the problems, putting scalability techniques into practice, maintaining

documentation standards, and setting up training sessions, seminars, and workshops. Such measures have the

potential to increase the efﬁcacy of resolving the problems.

1 INTRODUCTION

Software modeling in model-based software design

converts abstract models into tangible ones, which

facilitates pre-implementation system comprehension

(Troya et al., 2021)(Pourali and Atlee, 2018). These

models, which act as copies of the software that needs

to be developed, make system testing easier and pro-

vide insightful information for early defect discov-

https://orcid.org/0009-0009-6049-8182

https://orcid.org/0009-0004-5721-0276

https://orcid.org/0009-0007-5421-9664

https://orcid.org/0009-0005-4115-0106

https://orcid.org/0009-0005-4570-695X

https://orcid.org/0009-0008-8041-6183

https://orcid.org/0009-0005-1895-1044

https://orcid.org/0000-0002-9037-0181

ery (Troya et al., 2021). Since its beginnings with

UML in the 1990s, model-driven engineering (MDE)

has advanced dramatically (Pourali and Atlee, 2018).

While practitioners anticipate that model-driven en-

gineering (MDE) or system modeling will provide

an effective development route, they run into road-

blocks that prevent operations from running smoothly

(Feichtinger and Rabiser, 2020). Difﬁculties go be-

yond the system model itself and include a lack

of data regarding how it is used (Badreddin et al.,

2018). Although there has been some research on

these issues, there is still a dearth of perspectives

from Bangladeshi software companies in the litera-

ture. The main challenges (discussed in detail later)

that we found through previously done researches and

different literatures are as follows:

• No previous use cases or data to help build the

Akthar, S., Islam, M., Hasan, M., Siddiqua, M., Haque, S., Saad, J., Sadia, F. and Hasan, M.

Overcoming Obstacles in Model-Driven Engineering: Lessons from the Software Industry.

DOI: 10.5220/0012805800003753

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 19th International Conference on Software Technologies (ICSOFT 2024), pages 153-160

ISBN: 978-989-758-706-1; ISSN: 2184-2833

153

model (Badreddin et al., 2018).

• Couldn’t scale the model for the full system

(Badreddin et al., 2018).

• Different modules of the model couldn’t be

merged due to inconsistency in design (Badreddin

et al., 2018).

• Unconventional to the company’s usual system

(Feichtinger & Rabiser, 2020)

• Bad model quality (Badreddin et al., 2018).

The study aims to assess and pinpoint areas for

development regarding the difﬁculties faced by prac-

titioners when applying software modeling method-

ologies. Understanding the factors impacting soft-

ware modeling is one of the survey’s goals; doing so

could improve the approaches used. Events such as

the Grand Challenges in MDE workshop and MoD-

ELS have shed light on the current state of MDE

and its evolution (Badreddin et al., 2018)(Troya et al.,

2021). This research seeks to rectify the deﬁciency

of Bangladeshi software developers’ viewpoint in the

existing literature. Model alignment and system inte-

gration are two unresolved difﬁculties that MDE still

faces, despite its importance for interoperability (Ak-

dur et al., 2018) . If these issues are resolved well,

MDE adoption may increase and project failure rates

may decrease. For the purpose of the study, a sur-

vey was conducted that included software develop-

ers, software project managers, software architects,

software test engineers working in different software

ﬁrms in Bangladesh. The online survey was based

on a questionnaire focusing on the following areas of

concern along with other relevant research questions.

RQ1. What levels of difﬁculties do practition-

ers encounter with the various types of software

modeling challenges?

RQ 2. What unique challenges might practi-

tioners go through when modeling software sys-

tems?

The purpose of this study is to examine present

practices and identify the difﬁculties that practition-

ers face while applying model-based engineering in

Bangladesh’s software sector. By highlighting these

issues,contribution of this study:

• Identiﬁcation of present practices and challenges

faced by practitioners applying model-based en-

gineering in Bangladesh’s software sector.

• Utilization of our analysis to examine challenges

confronted by software modeling practitioners.

• Proposal of a framework to guide future endeav-

ors in addressing challenges and advancing sys-

tem modeling practices in Bangladesh’s software

sector.

2 LITERATURE REVIEW

Badreddin Badreddin et al. observed a ten-year

trend indicating increased adoption of formal and

domain-speciﬁc modeling languages, suggesting a

growing prevalence of modeling practices (Badred-

din et al., 2018). Troya et al. reviewed deﬁning

uncertainty in software models, presenting a classi-

ﬁcation framework to compare proposals and iden-

tify trends (Troya et al., 2021). Pourali and Atlee

investigated challenges in modifying and debugging

UML models, focusing on Class and State-Machine

diagrams, identifying contextual memorization and

debugging model ﬂaws as primary obstacles(Pourali

and Atlee, 2018). Feichtinger & Rabiser outlined

a transformation strategy for variability models, fo-

cusing on meta models and generic transformation

operations(Feichtinger and Rabiser, 2020). Akdur

et al. surveyed engineers to investigate modeling

practices in the embedded software industry, ﬁnding

the Sketch/No Formal modeling language commonly

used (Akdur et al., 2018). Rukhsara et al. intro-

duced a cloud-based e-commerce model designed for

Bangladesh, aiming to facilitate application develop-

ment and foster e-business growth(Rukhsara et al.,

2016). Baresi et al. discussed challenges in mobile

software engineering, emphasizing the importance

of mobile computing research(Baresi et al., 2020).

Khan and Khan proposed a Software Security As-

surance Model (SSAM) to aid Global Software De-

velopment (GSD) vendors in secure software devel-

opment(Khan and Khan, 2018). Tantithamthavorn

and Hassan discussed challenges in software model-

ing and defect analytics, stressing data quality and

well-documented modeling scripts(Tantithamthavorn

and Hassan, 2018). Bucchiarone et al. reviewed

the evolution of Model-Driven Engineering (MDE)

research, presenting grand challenges (Bucchiarone

et al., 2020). Rahim et al. surveyed software engi-

neering practices in Bangladesh, focusing on process

models, SDLC standards, and communication meth-

ods(Rahim et al., 2017). Amershi et al. mapped chal-

lenges for machine learning applications in software

engineering, highlighting issues like the oracle prob-

lem(Amershi et al., 2019).

3 RESEARCH METHODOLOGY

Sample Proﬁle: The survey data was collected

from 15-25 participants in Bangladesh-based soft-

ware companies, including project managers, devel-

opers, architects, solution architects, and testing engi-

neers with 1-5 years of experience. Table 1 details the

ICSOFT 2024 - 19th International Conference on Software Technologies

154

demographics, industry types, and employment disci-

plines of the participants.

Table 1: Participants Demographic.

Field of Practice Industry Type Type of Product

Developed

Software Test En-

gineer

E-commerce Web Applications

Project Manager Finance and

Banking

Mobile Applica-

tions

Software Test En-

gineer

E-commerce IoT (Internet of

Things) Applica-

tions

Software Devel-

oper

Healthcare Enterprise Appli-

cations

Project Manager Finance and

Banking

Others

Solution Architect Healthcare Web Applications

Project Manager Finance and

Banking

Web Applications

Software Archi-

tect

E-commerce Web Applications

Software Devel-

oper

E-commerce Web Applications

Software Devel-

oper

Others Enterprise Appli-

cations

Software Devel-

oper

Finance and

Banking

Web Applications

Software Archi-

tect

Finance and

Banking

Web Applications

Software Test En-

gineer

Finance and

Banking

Web Applications

Software Test En-

gineer

E-commerce Mobile Applica-

tions

Software Devel-

oper

Healthcare Desktop Applica-

tions

Software Test En-

gineer

E-commerce Mobile Applica-

tions

Data Collection and Analysis Method: Informa-

tion was gathered using Google Forms, with a ques-

tionnaire based on current literature. The survey was

distributed via email and messaging services. Re-

sponses were analyzed for patterns and trends, with

results visualized through data quantiﬁcation.

Limitation of Research Design: The study fo-

cuses on challenges in the Bangladeshi software sec-

tor regarding model quality, scalability, and industrial

acceptance in Model-Driven Engineering (MDE). It

aims to provide solutions and facilitate MDE adop-

tion, helping practitioners and researchers improve its

effectiveness. The concepts which are needed to con-

duct this study includes :-

- An understanding of how the software modeling

process works

- A good insight of the difﬁculties that practition-

ers undergo when practicing software modeling

- A thorough survey was carried out among dif-

ferent software businesses in Bangladesh to obtain

insights into the obstacles associated with software

modeling. In order to overcome the constraints of ear-

lier research and guarantee wide representation within

the development community, the questionnaire was

modiﬁed. For instance

• Do you implement system modeling in your

projects?

• In which phase(s) of the software development

life cycle do you use modeling?

• What are the main challenges you faced when us-

ing system modeling for any project?

• Does System Modeling help facilitate the devel-

opment of software?

• Do you face any difﬁculties using modeling lan-

guage(s)?

With a focus on evaluating current practices and

gathering insights on the efﬁcacy of software mod-

eling, practitioner satisfaction, and challenges faced

in model-based software systems, a thorough analy-

sis of survey data aims to uncover practitioners’ chal-

lenges and perceptions regarding software modeling.

Future plans involve incorporating practitioner feed-

back, broadening the survey scope, and implementing

solutions to further understand and address identiﬁed

difﬁculties.

4 FINDINGS/RESULTS

The demographic data in illustrates the various roles

that are involved in software development. It high-

lights the importance of quality assurance for Soft-

ware Test Engineers, the critical role project managers

play in project management, and the software designs

that are implemented by Software Developers. The

strategic vision and technical know-how of solution

and software architects help to create scale-able, re-

liable software solutions that support organizational

objectives.

Since most of the respondents are in their early

stages of their careers and have fewer than ﬁve years

of experience, their knowledge with the sector and

technological aptitude may be affected. The popula-

tion questioned is from a variety of industries, with

e-commerce, banking, ﬁnance, and healthcare hav-

ing a signiﬁcant presence. A comprehensive range

of software projects, such as web and mobile appli-

cations, Internet of Things applications, and business

solutions, demonstrate the sample’s competency.

Usage of System modeling and modeling lan-

guage and challenges face: System modeling

Overcoming Obstacles in Model-Driven Engineering: Lessons from the Software Industry

155

Table 2: Practice Items and their Adaptation Rate.

Annotation Practice Item Total Mean Adaptation Variance

points rate (%)

P1 System Modeling helps facilitate the development of

a software.

14 3.5 70 0.33

P2 How MDE affects personal experience (Productiv-

ity)?

13 3.25 65 0.25

P3 How MDE affects personal experience (Problem

Solving)?

13 3.25 65 0.25

P4 How MDE affects personal experience (Creativity)? 14 3.5 70 0.33

P5 How MDE affects personal experience (Enjoyment)? 12 3 60 0.67

P6 The level of challenges you are facing for Managing

language complexity.

12 3 60 0.67

P7 The level of challenges you are facing for Extending

Modeling languages.

12 3 60 0.67

P8 The level of challenges you are facing for Domain-

speciﬁc modeling environments?

11 2.75 55 0.92

P9 The level of challenges you are facing for Developing

formal modeling languages?

12 3 60 0.67

P10 The level of challenges you are facing for Analyzing

models?

13 3.25 65 0.927

P11 The level of challenges you are facing for Supporting

separation of design concerns?

14 3.5 70 0.33

P12 The level of challenges you are facing for Transform-

ing models?

11 2.75 55 0.25

P13 The level of challenges you are facing for Managing

models?

13 3.25 65 0.92

P14 System Modeling helps facilitate the development of

a software.

14 3.5 70 0.33

is widely used by respondents across the Soft-

ware Development Life Cycle (SDLC), with popu-

lar languages like Matlab/Simulink and SysML be-

ing advantageous and complying with business rules.

Nonetheless, difﬁculties like complexity and a dearth

of information or assistance are frequently mentioned.

Programming languages like Python and diagrams

like process and deployment diagrams are frequently

used in conjunction with specialized software like

Matlab and general tools like SQL Database Mod-

eler. Some res-ponders have mentioned problems

with model-driven approaches’ scalability and inte-

gration.

Practice items and their adaptation rate and

variance: The adaptation rate and variance pro-

vided in table 2 offer valuable insights into the ex-

tent to which the surveyed individuals have applied

or adapted various system modeling practices in their

respective ﬁelds of practice.

Adaptation Rate (%): The adaptation rate rep-

resents the percentage of professionals who have em-

ployed methods in their work and offers information

about the level of acceptance in each ﬁeld. Higher

adaptation rates indicate that system modeling tech-

niques are more widely accepted, which may empha-

size how valuable and effective they are thought to be

in improving software development processes.

Variance (%): Variance is a measure of how con-

sistently or diversely system modeling methodolo-

gies are used by practitioners; lower variance denotes

more uniform adoption. Reduced adaptation rates

could indicate that there are technological obstacles

or knowledge gaps preventing these strategies from

being successfully integrated. Low variance is seen in

Practice Items P2, P3, P4, P11, and P14, suggesting

that there is a general consensus regarding the advan-

tages of Model-Driven Engineering (MDE) in terms

of creativity, productivity, and facilitation of system

modeling and problem-solving.

Table 3: Field of practice and their Adaptation Rate.

Field of Total Mean Adaptation Variance

Practice (Out of 70) (%)

Project 43 3.07 61.42 0.53

Manager

Software 45 3.21 64.28 0.18

Architect

Software 36 2.57 51.42 0.26

Test Engineer

Software 54 3.85 77.14 0.13

Developer

Field of practice and their Adaptation Rate and

variance: Table 3 offers insights into the adapta-

tion rates and variations in system modeling practices

across different professional roles within the surveyed

ICSOFT 2024 - 19th International Conference on Software Technologies

156

Figure 1: Polynomial Regression of Adaptation rate of prac-

tice Items.

population.

Adaptation Rate (%): The adaptation rate pro-

vides insights into the percentage of practitioners

within each ﬁeld who have integrated system mod-

eling practices into their work, with Software Devel-

opers showing the highest adaptation rate at 77.14%.

Variance (%): The variance reﬂects the range of

adaptation rates within each professional ﬁeld, with

Software Developers showing a more uniform level of

adoption (variance of 0.131868) compared to Project

Managers, who display greater variance in adoption

rates (variance of 0.532967).

The differing adaption rates and differences be-

tween various professional positions (e.g., software

developers and software test engineers) point to

unique opportunities and problems for encouraging

the use of system modeling that are catered to the re-

quirements and limitations of each role. Taking care

of these things can encourage creativity and team-

work, improving software development procedures

and results for a variety of jobs inside companies.

With an R2 value of 0.3585, the polynomial re-

gression equation in Figure 1 is y = 0.2095x

−

3.296x + 73.104, meaning that x components ac-

count for approximately 35.85% of the variance in

adaption rate. With an R2 value of 0.5624, the

polynomial regression equation in Figure 2 is y =

5.7143x

− 25.143x + 83.571, which accounts for

roughly 56.24% of the variance in adaption rate.

Cronbach’s Alpha: Strong internal consistency

among survey items pertaining to system modeling

approaches is indicated by the high Cronbach’s alpha

value (0.93007), which suggests that the survey effec-

tively measures a shared underlying notion.

α =

k − 1

1 −

∑

i=1

(1)

Figure 2: Polynomial Regression of adaptation rate in ﬁeld

of practice.

where:

α : Cronbach’s alpha coefﬁcient

k : Number of items (questions)

: Variance of the scores on item i

: Variance of the total scores

This highlights how system modeling is becom-

ing more and more accepted in Bangladeshi software

companies for all jobs assessed. Even if the major-

ity of respondents think system modeling helps with

development and actively work to put it into practice,

problems still arise.

RQ1. What levels of difﬁculties do practitioners

encounter with the various types of software model-

ing challenges? The survey responses reﬂect differ-

ent levels of difﬁculties for practitioners. For Mod-

elling language 50% said that it is too complicated to

understand while 75% mentioned lack of proper doc-

umentation/ tutorial/ support as a difﬁculty. Manag-

ing language complexity increases the level of chal-

lenges for practitioners according to 33.3% respon-

dents. Extending Modelling languages adds another

level of difﬁculty for 35.7%. Domain-speciﬁc mod-

elling environments and developing formal modelling

languages were mentioned as difﬁculties by 42.9%.

Among the respondents 50% said Analysing models,

71.4% said Supporting separation of design concerns,

42.9% mentioned Transforming models and 66.7%

said Managing models were difﬁcult. The difﬁculty

levels observed in the survey responses differ from

one another but almost all of them are on the higher

side. It can be said that the level of difﬁculty faced by

the practitioners while working with system modeling

is high.

RQ2. What unique challenges might practition-

ers go through when modeling software systems?

According to our analysis software modeling for or-

ganizations presents signiﬁcant challenges for prac-

titioners globally, not just in Bangladesh. These in-

clude design inconsistencies, scalability issues, lack

Overcoming Obstacles in Model-Driven Engineering: Lessons from the Software Industry

157

Figure 3: Percentage of difﬁculty faced by the practitioners.

Figure 4: Challenges Faced by the practitioners while im-

plementing system modelling.

of prior use cases or data, inadequate resources, and

limited experience. These challenges hinder inte-

gration of modules into larger systems and high-

light the need for comprehensive solutions to improve

adoption and effectiveness of system modeling prac-

tices. To overcome challenges in implementing sys-

tem modeling, it is essential to comprehend the view-

points of practitioners in Bangladesh.

Not with standing these misgivings, the poll

shows that system modeling is becoming more widely

accepted, with a sizable portion of respondents be-

lieving in its advantages. Coordinated efforts in or-

ganizational support, training, and research and de-

velopment are needed to overcome obstacles. Stake-

holder engagement, resource provision, and training

are also necessary. Although system modeling is be-

coming more popular, considerable technological and

cultural adjustments are still required, as some practi-

tioners choose not to use it in order to save time.The

framework to handle difﬁculties and promote system

modeling methods is suggested by the discussion of

global challenges, the increasing acceptance of sys-

tem modeling. This emphasizes how crucial it is to

involve stakeholders, provide resources, and provide

Figure 5: The percentage for adopting system modeling by

practitioners.

training in order to overcome barriers and encourage

the use of system modeling techniques.

Even though the usage percentage of implement-

ing system modeling is promising, the percentage

of the system modeling not being used is not ne-

glectable.

5 PROPOSED GUIDELINES

The challenges in system modeling faced by practi-

tioners in Bangladesh are reﬂected globally, necessi-

tating comprehensive research to uncover their root

causes and improve understanding through detailed

documentation and comparative analysis. The abil-

ity to handle very large models for system of sys-

tems and Ultra-Large-Scale (ULS) systems is one

of the main challenges of Model-Driven Engineering

(MDE). Solutions like modular engineering princi-

ples, incremental processing, and logic inference en-

gines, alongside emphasizing the importance of doc-

umenting MDE processes and showcasing real-world

use cases at seminars to motivate practitioners and

foster broader adoption of system modeling. Cur-

rently, the trend is positive, that is, more developers

prefer to use system modeling. In the future, an in-

depth survey can be conducted based on all these new

projects being done.

6 PROPOSED MODEL

In the context of Model-Driven Engineering (MDE),

the challenges outlined can be addressed through var-

ious strategies and practices within the MDE frame-

work:

Identify Challenges: Addressing the challenges

requires a multi-faceted approach involving collab-

oration between researchers, practitioners, industry

stakeholders, and educational institutions.

ICSOFT 2024 - 19th International Conference on Software Technologies

158

Figure 6: Sequential Approach to Address Challenges in Model-Driven Engineering (MDE).

Table 4: Steps proposed to address the challenges in Model-

Driven Engineering (MDE).

Speciﬁc Practices Artifacts

Recognize the challenges faced in identiﬁed

MDE, which are not unique to Challenges

Bangladesh but prevalent globally

Conduct thorough research to Extensive

identify the root causes of the Research to

challenges in MDE Identify Root

Causes

Examine speciﬁc scenarios Investigation into

encountered during MDE Speciﬁc

implementation to understand the Implementation

challenges Scenarios

Establish comprehensive Documentation

documentation practices to record all Practices

steps taken in MDE

Implement real-life use cases as Utilization of

successful examples to encourage Real-Life Use

MDE adoption Cases

Arrange educational events to Organizing

promote understanding and adoption Seminars,

of MDE Workshops, and

Training Sessions

Encourage the adoption of MDE Industrial

practices in industrial settings to Adoption and

gather more data for research Implementation

Extensive Research to Identify Root Causes:

To comprehend the fundamental reasons behind the

difﬁculties encountered in system modeling, MDE

practitioners had to conduct thorough research. To

go deeper into the topics, this entails examining pre-

viously published material, performing empirical ex-

periments, and working with other academics.

Investigation into Speciﬁc Implementation Sce-

narios: MDE practitioners must investigate sce-

narios while system modeling is put into practice.

Through the analysis of real-world situations, profes-

sionals can acquire a deeper understanding of prag-

matic issues and create efﬁcient solutions customized

for various settings. Implementation of Scalability

Techniques: Scalability solutions such as modular en-

gineering principles, incremental processing, caching

mechanisms, and indexing strategies should be used

by practitioners because MDE requires handling big

models. These methods make it possible to han-

dle and manipulate big models efﬁciently, especially

when modeling Ultra-Large-Scale (ULS) and systems

of systems.

Documentation Practices: To record the actions

made during the modeling process, proper documen-

tation is crucial in MDE. Documentation is an impor-

tant tool for future reference and learning, as well as

for facilitating information sharing among practition-

ers and preserving best practices.

Utilization of Real-Life Use Cases: MDE prac-

titioners ought to use actual use cases as examples

of how to successfully apply system modeling. By

showcasing practical uses and measurable beneﬁts,

practitioners may foster conﬁdence and encourage the

widespread deployment of MDE techniques.

Organizing Seminars, Workshops, and Train-

ing Sessions: System modeling organizations and

companies should host training sessions, workshops,

Overcoming Obstacles in Model-Driven Engineering: Lessons from the Software Industry

159

and seminars to share knowledge and encourage prac-

titioners’ skill development. These gatherings pro-

vide chances for knowledge sharing, networking, and

practical learning.

Industrial Adoption and Implementation: For

producing empirical data and practical insights that

can guide future research and development endeav-

ors, the industrial adoption of MDE processes is es-

sential. Through the incorporation of MDE into in-

dustrial workﬂows and projects, professionals can add

to the increasing amount of evidence in the area and

promote ongoing progress.

By addressing these challenges within the frame-

work of MDE, practitioners can enhance the effec-

tiveness, scalability, and adoption of system modeling

techniques, ultimately advancing the state of the art in

engineering and software development.

7 CONCLUSIONS

Software modeling is essential to software develop-

ment, but there are a number of obstacles that busi-

nesses must overcome, such as the requirement for

accurate, thorough, and stakeholder-aligned models.

Practitioners need to acquire the required abilities,

comprehend the foundations of software modeling,

and keep up with industry changes in order to over-

come these obstacles. In order to successfully use

system modeling and possibly enhance the results of

software development, it is imperative that these is-

sues be resolved. The present study sheds light on the

challenges encountered by software modeling experts

in Bangladesh, underscoring the necessity for addi-

tional investigation and advancement to augment the

caliber and efﬁciency of software modeling.

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