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All Journal Scientific Journal of Engineering Research Scientific Journal of Computer Science
Atta Ur Rahman
Nanjing University of Information Science and Technology
Computer Science & IT Engineering

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Software Ecosystem Architectural Challenges and Mitigation Strategies: A Systematic Literature Review Inayat Ur Rahman; Atta Ur Rahman; Sara Shahzad; Sajid Ur Rahman
Scientific Journal of Computer Science Vol. 2 No. 2 (2026): December Article in Process
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjcs.v2i2.2026.456

Abstract

Software ecosystems (SECO) play a crucial role in modern software development by enabling accelerated innovation, collaboration among multiple stakeholders, and efficient utilization of shared resources and technologies. However, achieving these benefits requires robust, adaptable, and well-structured architectural design and management. Despite their importance, SECO architectures face several critical challenges, including interface instability, security vulnerabilities, scalability limitations, governance complexity, sustainability concerns, and evolving ecosystem dynamics. Although prior studies have explored individual aspects of SECO, there is a clear research gap in providing a comprehensive and systematic synthesis of architectural challenges and their corresponding mitigation strategies. In particular, no systematic literature review (SLR) has thoroughly examined these issues in an integrated manner. To address this gap, this study aims to systematically identify, categorize, and analyze architectural challenges in SECO and evaluate existing mitigation techniques. A structured SLR methodology is employed to collect, assess, and synthesize relevant literature, leading to the development of a conceptual framework that organizes both challenges and solutions. The findings reveal that key mitigation strategies—such as modularization, variability management, custom design approaches, and sandboxing—can significantly improve architectural stability, scalability, and sustainability. These results provide valuable insights for both researchers and practitioners by offering a consolidated understanding of SECO architectural issues and practical guidance for designing more resilient and sustainable software ecosystems.
A Study of Loss Weight Balance in Lightweight Self-Distilled Crowd Counting Muhammad Raza; Atta Ur Rahman; Pandula Pallewatta; Inayat Ur Rahman; Sahib Bahadar
Scientific Journal of Engineering Research Vol. 2 No. 3 (2026): September (in Process)
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v2i3.2026.493

Abstract

Lightweight crowd counting is important for real-time surveillance and resource-constrained deployment, where both computational efficiency and effective supervision are required. Although teacher-free self-distillation can improve lightweight density-regression models by guiding intermediate representations without an external teacher, the influence of composite loss weights in such frameworks has not been sufficiently analyzed. This paper presents a focused coefficient-wise loss-weight analysis within the Lightweight Self-Knowledge Distillation framework for single-image crowd counting. Instead of proposing a new architecture, the study investigates how the coefficients α, β, γ, and λ₂ affect optimization behavior and counting accuracy under a fixed experimental setup on ShanghaiTech Part B. Specifically, α controls intermediate feature alignment, β controls consistency supervision, γ controls direct density-regression supervision, and λ₂ controls the structural similarity term in the regression loss. The results show that moderate values of α and β improve performance by providing useful internal regularization, while excessive auxiliary weighting can slightly degrade accuracy. The analysis also indicates that γ should remain dominant because direct density-map regression is the primary learning signal. The best observed configuration is α = 6.0, β = 2.0, γ = 13.0, and λ₂ = 0.2, achieving 8.94 MAE and 11.51 RMSE on ShanghaiTech Part B. These findings highlight the importance of balanced supervision design within the evaluated LSKD framework on ShanghaiTech Part B.
Co-Authors Inayat Ur Rahman Muhammad Raza Pandula Pallewatta Sahib Bahadar Sajid Ur Rahman Sara Shahzad