Discovery of Modularity in Monolithic Java Project Codes Using Complex Networks

Authors

  • Marcos Cordeiro de Brito Jr Graduate Program in Electrical Engineering and Computing, Mackenzie Presbyterian University, São Paulo, Brazil, 01.302-907
  • Calebe P. Bianchini Graduate Program in Electrical Engineering and Computing, Mackenzie Presbyterian University, São Paulo, Brazil, 01.302-907
  • Leandro A. Silva Graduate Program in Electrical Engineering and Computing, Mackenzie Presbyterian University, São Paulo, Brazil, 01.302-907

DOI:

https://doi.org/10.13052/jwe1540-9589.2463

Keywords:

Monolithic, modules, complex network, Java, complexity, static analysis

Abstract

Monolithic architecture is a software design which brings significant difficulties to system developers when it comes to maintenance or expanding the scope of a project. On the other hand, a modular project consists of several similar entities, or modules, which are the object of similar functions or processes that, applied repeatedly, have well-defined classes and smaller modules to work, bringing benefits such as reduced project development time and increased productivity for the system developers. This work proposes the use of complex networks through the NetworkX library in Python, using modularity detection algorithms for the static analysis of Java code. The goal is to discover modules by analyzing dependencies between classes, indicating the best way to identify code clusters to be treated as modules automatically. The outcomes of applying the Greedy Modularity, Louvain, K-Clique, and Girvan Newman algorithms to two open-source projects will be presented. A comparative analysis of these results will be illustrated using generated graphs and a distribution map, emphasizing the number of communities identified by each algorithm.

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Author Biographies

Marcos Cordeiro de Brito Jr, Graduate Program in Electrical Engineering and Computing, Mackenzie Presbyterian University, São Paulo, Brazil, 01.302-907

Marcos Cordeiro de Brito Jr received his Master’s degree in Electrical Engineering and Computing from Universidade Presbiteriana Mackenzie in June 2023. Currently, he is the Head of Technology at SPC Grafeno, São Paulo, Brazil. He has had extensive experience in systems and software development since 2000. His skills include technical leadership, agile development, and continuous integration with tools like Jenkins, Docker, Kubernetes, and Git. Marcos is proficient in various programming languages, including Java, Python, and C#, and has strong knowledge of frameworks such as Spring Boot, Django, and Flask. Additionally, he has experience in agile methodologies, TDD, BDD, and cloud architectures using Google Cloud and AWS. He has also served as a professor at Paulista Faculty of Informatics and Administration (FIAP), teaching topics such as Spring Boot, MVC architecture, and Hibernate. Throughout his career, Marcos has participated in numerous national and international projects, significantly contributing to software innovation and quality in the organizations he has worked for.

Calebe P. Bianchini, Graduate Program in Electrical Engineering and Computing, Mackenzie Presbyterian University, São Paulo, Brazil, 01.302-907

Calebe P. Bianchini holds a Bachelor’s degree in Computer Science from UFSCar, Brazil, where he also obtained his Master’s degree. He completed his Ph.D. in Computer Engineering at POLI/USP, Brazil, in 2009. With a combined experience of over 19 years in high-performance computing, he has contributed to numerous research and development projects with Petrobras, Shell, Intel, and CERN, among others. Currently, he is an Associate Professor and researcher at Mackenzie Presbyterian University, where he also heads the MackCloud Lab, a Scientific Computing Center. He is an active member of the Brazilian High-Performance Computing community, particularly in organizing competitions and challenges in this domain. Additionally, he is an Ambassador for the NVIDIA Deep Learning Institute, where he conducts courses related to high-performance computing on NVIDIA GPUs.

Leandro A. Silva, Graduate Program in Electrical Engineering and Computing, Mackenzie Presbyterian University, São Paulo, Brazil, 01.302-907

Leandro A. Silva has a degree in Computer Engineering, a Master’s degree and a Ph.D. from the USP Polytechnic School. He is currently a Professor at the Faculty of Computing and Informatics (FCI), in the Stricto-Sensu Academic Postgraduate Program in Electrical and Computer Engineering (PPGEEC), and in the Stricto-Sensu Professional Postgraduate Program in Applied Computing (PPGCA) and Coordinator Funding for Research (CFP) of the Dean of Research and Postgraduate Studies (PRPG) of Universidade Presbiteriana Mackenzie (UPM). At UPM he has already held administrative activities as FCI Extension Coordinator and PPGEEC Coordinator. As for research activities, he participates in Program Committees for national and international conferences and also provides services as a technical reviewer for conferences and specialized journals. As a line of research, he has worked mainly in areas involving data science such as artificial neural networks, machine learning, data mining, big data, and pattern recognition. In these areas, he has regularly published scientific articles in the main national and international congresses, as well as in specialized journals. He is the main author of the textbook on Data Mining by Elsevier. Finally, Prof. Leandro Augusto co-leads the Laboratory and Research Group Big MAAp – Big Data and Applied Analytical Methods, where he develops Research and Development Projects with support from public agencies and companies from different segments of the production sector.

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Published

2025-09-25

How to Cite

Brito Jr, M. C. de ., Bianchini, C. P. ., & Silva, L. A. . (2025). Discovery of Modularity in Monolithic Java Project Codes Using Complex Networks. Journal of Web Engineering, 24(06), 911–942. https://doi.org/10.13052/jwe1540-9589.2463

Issue

2025: Vol 24 Iss 6

Section

Articles