Recent Trends in Software Engineering - International Journal of Software Engineering & Applications (IJSEA)

The International Journal of Software Engineering & Applications (IJSEA) is a bi-monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Software Engineering & Applications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on understanding Modern software engineering concepts & establishing new collaborations in these areas.

1. Recent Trends in Software
Engineering - 2025

InternationalJournalofSoftware Engineering
& Applications (IJSEA)** ERA Indexed **

ISSN: 0975-9018 (Online); 0976-2221(Print)

https://www.airccse.org/journal/ijsea/ijsea.html

Citations, h-index, i10-index

Citations 4767 h-index 35 i10-index 113 i10-index113

2. MODEL CHECKING FOR MULTI-AGENT SYSTEMS
MODELED BY EPISTEMIC PROCESS CALCULUS

Qixian Yu
1
, Zining Cao
1,2,3*
, Zong Hui
1,4
and Yuan Zhou
1

1
College of Computer Science and Technology, Nanjing University of Aeronautics and
Astronautics, Nanjing 211106, P. R. China
2Ministry Key Laboratory for Safety-Critical Software Development and
Verification, Nanjing 211106, P. R. China.
3Collaborative Innovation Center of Novel Software Technology and
Industrialization, Nanjing 210023, P. R. China
4Faculty of Computer and Software Engineering, Huaiyin Institute Of Technology,
Huaian 223001, P. R. China

ABSTRACT

This paper presents a comprehensive framework for modeling and verifying multi-agent systems.
The paper introduce an Epistemic Process Calculus for multi-agent systems, which formalizes the
syntax and semantics to capture the essential features of agent behavior interactions and epistemic
states. Building upon this calculus, we propose ATLE, an extension of Alternating-time Temporal
Logic incorporating epistemic operators to express complex properties related to agent epistemic
state. To verify ATLE specifications, this paper presents a model checking algorithm that
systematically explores the state space of a multi-agent system and evaluates the satisfaction of
the specified properties. Finally, a case study is given to demonstrate the method.

KEYWORDS

Multi-agent System, Epistemic Logic, Value-Passing CCS, ATL, Model Checking

For More Details : https://aircconline.com/ijsea/V16N1/16125ijsea01.pdf

Volume Link : https://airccse.org/journal/ijsea/vol16.html

3. REFERENCES

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[7] Ahmad R, Rahimi S, Gupta B( 2007). An Intelligence-Aware Process Calculus for Multi-Agent System
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5. COUPLING METRICS FOR ASPECT
ORIENTED SOFTWARE

Kelvin Mutunga Katonyi
1
, John Gichuki Ndia
1
, Geoffrey Muchiri Muketha
2


1
Department of Information Technology, Murang'a University of Technology, Kenya
2
Department of Computer Science, Murang' a University of Technology, Kenya

ABSTRACT

The Aspect Oriented Software (AOS) paradigm emerged as a response to the limitations of
Object-Oriented Programming, specifically its inability to modularize cross-cutting concerns
effectively. However, AOS have inherent complexity that keeps increasing as software is
modified and most of the existing metrics have not been theoretically or empirically validated.
This means we cannot rely on them for measurement of AOS complexity. This paper proposes
four base metrics and two composite coupling metrics for analyzing the complexity of AOS. The
metrics were derived using the Entity-Attribute-Metric-Tool (EAMT) model. The metrics were
theoretically validated using Briand’s framework, and a tool was developed to automate the
computation of these metrics. Theoretical results indicate that the proposed metrics are
mathematically sound. A between-subjects experimental study was conducted to validate the
proposed metrics and results indicate that the proposed metrics are strongly correlated with
modularity, meaning they are important for modularity assessment in AOS-based software.

KEYWORDS

Aspects, aspect-oriented systems, model modularity, software metrics

For More Details : https://aircconline.com/ijsea/V16N1/16125ijsea02.pdf

Volume Link : https://airccse.org/journal/ijsea/vol16.html

6. REFERENCES

[1] T. Ishio, S. Kusumoto, and K. Inoue, “Application of Aspect-Oriented Programming to Calculation of
Program Slice,” Oct. 2002.
[2] A. Magableh, A. A. Saifan, A. Rawashdeh, and H. B. Ata, “Towards Improving Aspect-Oriented
Software Reusability Estimation,” In Review, preprint, Jul. 2023. doi: 10.21203/rs.3.rs-3124387/v1.
[3] M. Hocaoglu, “Aspect Oriented Programming Perspective in Software Agents and Simulation,”
International Journal of Advancements in Technology, vol. 08, Jan. 2017, doi: 10.4172/0976-
4860.1000186.
[4] M. I. Ghareb and G. Allen, “Quality Metrics measurement for Hybrid Systems (Aspect Oriented
Programming – Object Oriented Programming),” Technium, vol. 3, no. 3, pp. 82–99, Apr. 2021, doi:
10.47577/technium.v3i3.3261.
[5] M. Zhao, C. Zhou, Y. Chen, B. Hu, and B.-H. Wang, “Complexity versus modularity and
heterogeneity in oscillatory networks: Combining segregation and integration in neural systems,”
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modularity, and physical complexity,” Phys. Rev. E, vol. 82, no. 2, p. 026117, Aug. 2010, doi:
10.1103/PhysRevE.82.026117.
[7] J. G. Ndia, G. M. Muketha, and K. K. Omieno, “A Survey of Cascading Style Sheets Complexity
Metrics,” May 31, 2019, Rochester, NY: 3405783. doi: 10.2139/ssrn.3405783.
[8] K. A. Onyango, G. M. Muketha, and E. M. Micheni, “A Metrics-Based Fuzzy Logic Model for
Predicting the Reusability of Object-Oriented Software,” 2020, Accessed: Oct. 23, 2023. [Online].
Available: http://repository.mut.ac.ke:8080/xmlui/handle/123456789/4437
[9] K. A. Onyango, G. M. Muketha, and J. G. Ndia, “Structural Complexity Metrics for Laravel
Software,” International Journal of Software Engineering, 2024.
[10] L. C. Briand, S. Morasca, and V. R. Basili, “Property-based software engineering measurement,”
IEEE Trans. Software Eng., vol. 22, no. 1, pp. 68–86, Jan. 1996, doi: 10.1109/32.481535.
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[12] R. Burrows, A. Garcia, and F. Taïani, “Coupling Metrics for Aspect-Oriented Programming: A
Systematic Review of Maintainability Studies,” in Evaluation of Novel Approaches to Software
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in Computer and Information Science, vol. 69. , Berlin, Heidelberg: Springer Berlin Heidelberg,
2010, pp. 277–290. doi: 10.1007/978-3-642-14819-4_20.
[13] S. Misra and I. Akman, “Weighted Class Complexity: A Measure of Complexity for Object Oriented
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Electrical and Computer Engineering, Canadian Journal of, vol. 28(2), pp. 69–74, May 2003, doi:
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[15] A. W. King’ori, G. M. Muketha, and J. G. Ndia, “A Suite of Metrics for UML behavioral diagrams
based on complexity perspectives,” International Journal of Software Engineering, 2024.

Authors

Kelvin Mutunga Katonyi is System Administrator in the Directorate of Information
Technology, Murang’a University of Technology. He received his BSc. In Information
Technology from South Eastern Kenya University (SEKU), Kenya. He is currently
pursuing his MSc. Information Technology at Murang’a University of technology,
Kenya. His research interests mainly includes software metrics, software quality and
data.

7. John Gichuki Ndia is lecturer and Dean, School of Computing and Information
Technology at Murang’a University of Technology, Kenya. He obtained his Bachelor
of Information Technology from Busoga University, Uganda in 2009, his MSc. in
Data Communication from KCA University, Kenya in 2013, and his PhD in
Information Technology from Masinde Muliro University of Science and
Technology, Kenya in 2020. His research interests include software quality, software
testing, and computer networks and security. He is a Professional Member of the
Institute of Electrical and Electronics Engineers (IEEE) and the Association for
Computing Machinery (ACM).

Geoffrey Muchiri Muketha is Professor of Computer Science and Director of
Postgraduate Studies at Murang'a University of Technology, Kenya. He received his
BSc. in Information Sciences from Moi University, Kenya in 1995, his MSc. in
Computer Science from Periyar University, India in 2004, and his PhD in Software
Engineering from Universiti Putra Malaysia in 2011. He has wide experience in
teaching and supervision of postgraduate students. His research interests include
software and business process metrics, software quality, verification and validation,
empirical methods in software engineering, and computer security. He is a member of
the International Association of Engineers (IAENG).

8. TOWARDS A ROBUST QUALITY
ASSURANCE FRAMEWORK FOR CLOUD
COMPUTING ENVIRONMENTS

Mohammed Ahmad Alharbi and Rizwan Qureshi

Department of Information Technology, Faculty of Computing and Information
Technology, King Abdul-Aziz University, Jeddah 80213, Saudi Arabia

ABSTRACT

Trends such as cloud computing raise issues regarding stable and uniform quality assurance (QA)
and validation of software requirements. Current QA frameworks are poorly defined, often not
automated, and lack the flexibility needed for on-demand, cloud-based environments. These gaps
lead to inconsistencies in service delivery, challenges in scaling organizational capacity, and
internal and external inefficiencies that affect the reliability and effectiveness of cloud services.
This paper presents a detailed framework for QA in cloud computing systems and advocates for
standardized, automated, and adaptable systems to address these challenges. It aims to establish
generic QA policies, incorporate intelligent techniques to enhance extendibility, and create
adaptive solutions to manage the inherent attributes of cloud computing environments. The
proposed framework is evaluated through survey questionnaires from industry practitioners, and
descriptive statistics summarize the results. The study demonstrates the promise, effectiveness,
and potential applicability of integrating a single QA framework to enhance the software’s
functionality, dependability, and future adaptability in cloud computing systems.

KEYWORDS

Cloud computing, quality assurance, automated systems, adaptive solutions, industry
practitioners

For More Details : https://aircconline.com/ijsea/V16N1/16125ijsea03.pdf

Volume Link : https://airccse.org/journal/ijsea/vol16.html

9. REFERENCES

[1] Ali, T., Al-Khalidi, M., & Al-Zaidi, R. (2024). Information Security Risk Assessment Methods in
Cloud Computing: Comprehensive Review. The Journal of Computer Information Systems, 1–28.
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[2] Atoum, I., Baklizi, M. K., Alsmadi, I., Otoom, A. A., Alhersh, T., Ababneh, J., Almalki, J., &
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[5] Calatrava, A., Hernán Asorey, Astalos, J., Azevedo, A., Benincasa, F., Blanquer, I., Bobak, M.,
Brasileiro, F., Codó, L., del Cano, L., Esteban, B., Ferret, M., Handl, J., Kerzenmacher, T., Kozlov,
V., Křenek, A., Martins, R., Pavesio, M., Rubio-Montero, A. J., & Sánchez-Ferrero, J. (2023). A
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[6] Dawood, M., Tu, S., Xiao, C., Alasmary, H., Waqas, M., & Rehman, S. U. (2023). Cyberattacks and
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[7] Dima, A., Bugheanu, A.-M., Boghian, R., & Madsen, D. Ø. (2022). Mapping Knowledge Area
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[8] Kedi, E., Ejimuda, N. C., & Ajegbile, M. D. (2024). Cloud computing in healthcare: A
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[9] Adeusi, O. C., Adebayo, Y. O., Ayodele, P. A., Onikoyi, T. T., Adebayo, K. B., & Adenekan, I. O.
(2024). IT standardization in cloud computing: Security challenges, benefits, and future directions.
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[10] Shrivastava, V., & Udgir, V. (2024). An Improved Intelligent Cloud-based Structure for Automated
Product Quality Control. International Journal of Latest Engineering Research and Applications, 9(1),
11–19. https://doi.org/10.56581/ijlera.9.1.11-19.

Authors

Prof. Dr. Rizwan Qureshi received his Ph.D. degree in Computer Sciences from
National College of Business Administration & Economics, Pakistan 2009. He is
currently working as a Professor in the Department of IT, King Abdulaziz University,
Jeddah, Saudi Arabia. This author is the best researcher awardees from the
Department of Information Technology, King Abdulaziz University in 2013 and
2016.

Mr. Mohammed Ahmad Alharbi is a master student in the Department of IT, King
Abdulaziz University, Jeddah, Saudi Arabia.

10. GREEN AND SUSTAINABLE CLOUD
COMPUTING: STRATEGIES FOR CARBON
FOOTPRINT REDUCTION AND ENERGY
OPTIMIZATION

Abirami Dasu Jegadeesh

Independant Researcher, Charlotte, USA

ABSTRACT

The quick growth of cloud computing has caused a revolution in industries allowing companies to
expand operations, boost productivity, and come up with new ideas at speeds never seen before.
Yet, this growth has brought big environmental effects. Data centers, which form the core of
cloud computing, are facilities that use a lot of energy and add a lot to global carbon emissions.
By 2030, experts think the energy needs of data centers in just the United States will triple
making up to 12% of the country's total power use (McKinsey, 2024). This big jump in energy
demand shows we need to adopt sustainable cloud computing methods to reduce environmental
harm while meeting industries' growing need for computing power. A big reason for this growing
energy need is the more complex tasks we're doing those powered by AI and machine learning.
These game-changing technologies need a lot of computing power, which puts more strain on
data centers and energy systems. Just the use of generative AI is likely to need 50 to 60 more
gigawatts (GW) of data center space in the U.S. by 2030 (McKinsey, 2024). This growth shows
the two-fold challenge: to scale up cloud systems while also cutting down their carbon output.

KEYWORDS

sustainable cloud computing, green, carbon foot print, energy optimization.

For More Details : https://aircconline.com/ijsea/V16N3/16325ijsea01.pdf

Volume Link : https://airccse.org/journal/ijsea/vol16.html

11. REFERENCES

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12. Authors

Abirami Dasu Jegadeesh

 Energetic, motivated, and diligent professional with 18years 2month(s)
Software Architecture & Development experience in Web and Mobile based
applications with good communication & interpersonal skills, a highly
motivated & productive individual.
 Adroit at learning new concepts quickly and communicating ideas effectively.
 Dedicated and highly determined to achieve personal goals as well as the
organizational goals.
 Ability to work both independently and as team to achieve results in stated standards.