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Software Engineering in Computing Systems
Published by Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia
ISSN : -     EISSN : 31240062     DOI : 10.66472
Core Subject :
Software Engineering in Computing Systems is a peer-reviewed academic journal that aims to advance research in software engineering practices and methodologies for the development of reliable, secure, and scalable computing systems, covering topics such as software architecture and system design, software development methodologies, software testing, verification and validation, DevOps and continuous integration and deployment, software engineering for embedded and real-time systems, secure and dependable software systems, as well as software maintenance and evolution. The journal is published in February, May, August, and November.
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Articles 8 Documents
 1 
Evaluating the Impact of Model Driven Development on Verification and Validation Efficiency in Secure, Large Scale Enterprise Software Systems Sandy Suryady; Siska Narulita; Amna Amna
Software Engineering in Computing Systems Vol. 1 No. 1 (2026): February: Software Engineering in Computing Systems
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/secons.v1i1.46

Abstract

Model driven Development (MDD) has emerged as an efficient software engineering methodology that focuses on using high-level models as primary artifacts throughout the software development process. The methodology involves transforming abstract models into detailed designs, and eventually into executable code, with the assistance of automated tools. This study evaluates the impact of MDD on the Verification and Validation (V&V) processes within secure enterprise software systems. By comparing MDD-based projects with traditional code-centric development approaches, the study highlights the advantages of MDD in reducing verification time, minimizing defect leakage, and improving the traceability of security requirements. MDD significantly enhances V&V efficiency by automating key processes, which allows for earlier error detection and better resource utilization. Additionally, MDD strengthens security compliance by integrating security requirements early in the development lifecycle, ensuring better alignment between system requirements and their implementation. Despite the clear benefits, challenges such as the lack of standardized tools and the need for specialized expertise in model development were also encountered during the study. The findings of this research offer important insights for enterprise software development teams looking to adopt MDD for more efficient and secure V&V processes. Future research should focus on the long-term impact of MDD on security compliance, as well as its adoption across different industries, to fully understand the practical benefits and challenges of implementing MDD in diverse real-world environments.
Framework for Integrating Continuous Integration and Continuous Deployment (CI or CD) with Automated Security Testing to Improve Software Dependability Syaiful Anwar; Irwanto Irwanto; Safrizal Safrizal
Software Engineering in Computing Systems Vol. 1 No. 1 (2026): February: Software Engineering in Computing Systems
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/secons.v1i1.47

Abstract

The increasing demand for rapid software delivery has led to the widespread adoption of Continuous Integration (CI) and Continuous Deployment (CD) pipelines. These pipelines automate the processes of code integration, testing, and deployment, significantly improving the speed and reliability of software development. However, traditional CI or CD pipelines often overlook security testing, leading to vulnerabilities in the deployed software. To address this gap, this study proposes an integrated framework that embeds automated security testing within the CI or CD process. The framework incorporates security testing tools such as Static Application Security Testing (SAST), Dynamic Application Security Testing (DAST), and Vulnerability Assessment and Penetration Testing (VAPT) to ensure continuous security checks throughout the development lifecycle. The experimental results show that the proposed framework enhances early vulnerability detection, with detection rates increasing from 30% to 70%. Additionally, the framework reduces deployment failures from 50% to 20%, demonstrating its effectiveness in improving software dependability. While the integration of automated security testing adds a slight 5% increase in pipeline execution time, this minimal impact does not significantly affect the overall speed of the pipeline. The proposed approach successfully balances security and efficiency, ensuring that software is both secure and delivered at high speed. This research highlights the importance of integrating security into CI or CD pipelines and demonstrates that it is possible to achieve high security without sacrificing the speed of software development. The study also discusses the practical implications for software development teams and suggests areas for future research, including the integration of advanced AI-driven security testing tools and the expansion of the framework's applicability across different software projects.
Empirical Analysis of Design Patterns, Modular Architecture, and Maintainability in Distributed Real Time Computing Systems under Agile Development Practices Agustinus Budi Santoso; Febryantahanuji Febryantahanuji; Atiek Nurindriani; Robiatul Adawiyah
Software Engineering in Computing Systems Vol. 1 No. 1 (2026): February: Software Engineering in Computing Systems
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/secons.v1i1.48

Abstract

This study investigates the relationship between design patterns, modular architecture, and the maintainability of distributed real time systems developed using agile practices. Distributed real time systems are critical in various sectors, including telecommunications, healthcare, and automotive, where strict timing constraints and reliability are essential. Agile methodologies, known for their flexibility and iterative development, have been widely applied to software engineering, but their impact on long-term system maintainability, especially in complex real time environments, has been insufficiently explored. This research employs an empirical analysis, combining both quantitative and qualitative data from multiple real time system projects using agile methods. The analysis focuses on the application of design patterns, such as Singleton, Observer, and Factory, and evaluates the effectiveness of modular architectures in enhancing system scalability, flexibility, and long-term sustainability. The study also explores how agile practices contribute to system performance and maintainability, despite challenges related to frequent updates and coordination among distributed teams. Key findings show a positive correlation between the consistent use of design patterns and modularity, which significantly improves the maintainability and adaptability of distributed real time systems. This research also highlights the challenges faced by agile methods in maintaining architectural consistency and managing non-functional requirements, particularly in distributed environments. The results contribute valuable insights into adapting agile practices to meet the specific demands of distributed real time systems, offering recommendations for developers and project managers to incorporate modular architecture and design patterns to enhance long-term system sustainability. Further research is suggested to explore new design patterns and investigate the broader impact of agile methodologies on system quality beyond maintainability.
Adaptive DevOps Practices for Enhancing Reliability and Performance of Embedded Computing Platforms in Safety Critical Industrial Applications Deasy Widyastomo; Yosef Lefaan; Irlon Irlon
Software Engineering in Computing Systems Vol. 1 No. 1 (2026): February: Software Engineering in Computing Systems
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/secons.v1i1.49

Abstract

This study investigates the adoption of adaptive DevOps practices in embedded systems used in safety-critical industrial applications. Traditional DevOps models, which are primarily designed for cloud-based systems, face significant challenges when applied to embedded platforms due to hardware constraints, real-time performance requirements, and stringent safety standards. The research focuses on developing a tailored DevOps framework that integrates continuous integration/continuous delivery (CI or CD) pipelines, automation, real-time monitoring, and safety assurance processes to enhance system reliability, performance, and compliance with regulatory standards. The study uses a case study methodology, involving embedded system teams across multiple industrial sectors, to assess the impact of these adapted DevOps practices on system stability and operational efficiency. Key findings show that the adoption of adaptive DevOps practices led to significant improvements in system reliability, performance, and deployment stability. Continuous feedback mechanisms allowed for early issue detection and faster resolution, leading to enhanced system uptime and responsiveness. Additionally, the integration of safety assurance into the DevOps pipeline ensured that safety-critical systems complied with required safety integrity levels and certification standards. The study further explores the integration of DevOps with embedded safety-critical systems, highlighting the benefits of cross-domain collaboration, enhanced communication, and the ability to address the unique challenges of these platforms. The research also underscores the limitations of conventional DevOps models in embedded systems and presents practical implications for the wider adoption of DevOps in safety-critical industrial applications. Future research is recommended to refine DevOps frameworks for embedded systems, integrating emerging technologies like the Industrial Internet of Things (IIoT) and Digital Twins to further optimize performance, security, and predictive maintenance.
A Comparative Study of Software Testing Techniques and Quality Metrics for Predicting Failure Rates in Scalable Cloud Native Software Systems Winny Purbaratri; Mujito Mujito; Sayyid Jamal Al Din
Software Engineering in Computing Systems Vol. 1 No. 1 (2026): February: Software Engineering in Computing Systems
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/secons.v1i1.50

Abstract

Cloud-native systems are essential for modern software development, offering enhanced scalability, flexibility, and resilience through cloud computing environments. However, ensuring the reliability and performance of these systems presents a challenge due to their dynamic and distributed nature. Traditional testing methods, such as unit and integration testing, while valuable for detecting individual component defects and interactions, are insufficient for predicting failure rates in complex, cloud-native applications. This study explores the effectiveness of various testing techniques and quality metrics in predicting failure rates within scalable cloud-native systems. A comparative experimental study was conducted using three primary testing techniques: unit testing, integration testing, and chaos testing. The results indicate that chaos testing, when combined with advanced quality metrics such as migration rate and mismigration rate, significantly outperforms traditional methods in predicting failure rates and evaluating system resilience. These findings suggest that chaos testing offers a more comprehensive evaluation, simulating real-world disruptions to test system behavior under stress, which is essential for cloud-native environments where high availability and fault tolerance are critical. The study also highlights the importance of integrating predictive quality metrics, which improve the accuracy of failure predictions and enhance system reliability. The study concludes that for cloud-native systems, a combination of advanced testing techniques and predictive metrics is essential for ensuring high availability, scalability, and reliability in dynamic environments. Future research should focus on refining predictive testing approaches, developing standardized frameworks, and empirically validating new testing methods to address the growing complexity of cloud-native systems.
Success Factors of Government Digital Applications in Public Service Delivery: A Systematic Literature Review Rifqi Fahrudin; Zainal Arifin Hasibuan; Bobby Kurniawan; Sri Supatmi
Software Engineering in Computing Systems Vol. 1 No. 2 (2026): May: Software Engineering in Computing Systems
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/secons.v1i2.395

Abstract

The rapid development of digital government applications has significantly transformed public service delivery; however, their success remains inconsistent due to the complexity of multiple influencing factors. Many government digital systems experience low adoption, usability challenges, and limited impact on service quality, indicating the need for a comprehensive understanding of the determinants of success. This study aims to identify and synthesize the critical success factors of government digital applications in public service delivery. To achieve this objective, a systematic literature review (SLR) was conducted using the Scopus database, applying a predefined search strategy and PRISMA-based screening process. From an initial set of 176 articles, 44 relevant studies were selected and analyzed using a coding framework to classify success factors into four dimensions: technological, organizational, user, and governance. The results show that digital government success is inherently multidimensional, with user-related factors such as trust, usability, and satisfaction emerging as the most dominant, while technological factors function as enabling components and organizational and governance factors ensure sustainability and effectiveness. Furthermore, the findings reveal significant research gaps, particularly the lack of integrated frameworks and the fragmented treatment of success factors in existing studies. This study concludes by proposing an integrated classification framework that provides a comprehensive understanding of digital government success and offers practical guidance for policymakers in designing more effective and sustainable digital public services.
The Integration Of Non-Academic Variables In Student Risk Assessment: A Conceptual Framework Hani Irmayanti; Eddy Soeryanto Soegoto; Hidayat Hidayat; Rio Yunanto; Zainal Arifin Hasibuan; Sri Supatmi
Software Engineering in Computing Systems Vol. 1 No. 2 (2026): May: Software Engineering in Computing Systems
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/secons.v1i2.435

Abstract

Students’ success in completing their studies on time is a vital indicator of the quality of higher education management in Indonesia. However, high dropout rates pose a major challenge, often caused by institutions’ failure to detect warning signs of academic failure in a timely manner. The main issue lies in the current evaluation approach, which is reactive and limited to conventional academic indicators such as the Grade Point Average (GPA), thereby neglecting the psychosocial factors that influence performance. This study aims to develop a more comprehensive conceptual framework for the early detection of academic failure risk by integrating academic and non-academic dimensions. The methodology used is adapted from the Design Science Research Methodology (DSRM), focusing on the stages from problem identification to the design of the model artifact. The proposed approach is a hybrid model that combines traditional academic variables with non-academic variables, including psychological stress levels, self-efficacy, and social support. The design results indicate that this framework is capable of identifying “latent pressure” as a leading indicator of failure before a decline in academic performance occurs. The synthesis of this study confirms that the integration of non-academic variables enhances the model’s transparency and provides a more meaningful and targeted interpretation of risk factors. In conclusion, this framework provides a theoretical foundation for educational institutions to transition from reactive evaluation to a system of personalized, proactive interventions. The implementation of this model is expected to improve student retention through earlier and more targeted risk mitigation.
Global Trends and Framework Development of AI and IoT Integrated Waste Automation for Emerging Economies Sri Erina Damayanti; Eddy Soeryanto Soegito; Estiko Rijanto; Hidayat Hidayat
Software Engineering in Computing Systems Vol. 1 No. 2 (2026): May: Software Engineering in Computing Systems
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/secons.v1i2.444

Abstract

Waste management in Indonesia faces extreme regional disparities, ranging from critical waste accumulation zones and circular economy transition zones to specific material deficit zones. The primary problem lies in the inability of conventional systems to process heterogeneous waste efficiently, which leads to the failure of sustainable environmental conservation. An intelligent solution is required to integrate physical technology with an adaptive policy evaluation system. This research develops a systematic framework for the development and evaluation of waste processing automation technology. The research stages begin with a Bibliometric Analysis and Systematic Literature Review (SLR) using metadata from Scopus and Web of Science to identify global trends via VOSviewer. Furthermore, this study integrates AI and IoT as primary instruments for nature conservation. Through the processing of large data volumes (Big Data) from IoT sensors, AI (such as Multi-Criteria Decision Making) performs predictive analysis to automatically evaluate three regional conditions. AI plays a crucial role in determining corrective actions, including optimizing the use of oxy-hydrogen (HHO) fuel in incinerators to suppress emissions and managing cross-regional waste logistics, thereby ensuring natural resources are preserved through precise and low-pollution waste elimination processes. This research generates intelligent governance patterns and actionable insights to guide system users, particularly local governments and industrial managers, in implementing appropriate waste processing technologies. This solution provides automated operational guidance that ensures energy efficiency and economic sustainability while maintaining ecosystem preservation through standardized waste processing based on the specific regional characteristics in Indonesia.

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