Open Access

The Architecture of Quality: Integrating Machine Learning, Blockchain, and Automated Analysis for the Evolution of Secure and Modular Software Systems

PDF

Alaric Whitemore ¹ ,

⁴ Department of Computer Science and Software Engineering, University of Zurich, Switzerland

Abstract

The contemporary software engineering landscape is characterized by a dual challenge: the necessity to maintain high-quality source code in increasingly complex, multi-party environments and the requirement to modernize legacy architectures through automated modularization. This research article provides an extensive investigation into the multifaceted dimensions of software code quality, ranging from introductory programming pedagogy to industrial-scale defect prediction using convolutional neural networks. By synthesizing diverse technological paradigms, including machine learning-assisted service boundary detection and blockchain readiness frameworks, this study evaluates how emerging technologies address the systemic vulnerabilities of fluid, open software ecosystems. The research delves into the empirical needs of developers regarding program analysis, the categorization of code defects in educational settings, and the implications of metric distributions on overall system reliability. Furthermore, the article explores the organizational and technological factors influencing the adoption of decentralized systems and cloud computing as foundational pillars for secure software development. Through a detailed analysis of error ranking, automated repair bibliographies, and service-oriented refactoring, this work proposes a holistic framework for enhancing the lifecycle of software systems, ensuring they remain robust, secure, and maintainable in an era of rapid technological transition.

Keywords

Software Code Quality, Machine Learning, Blockchain Adoption, Automated Program Analysis

References

Alzahrani, S., Daim, T., & Choo, K. R. (2022). Assessment of the blockchain technology adoption for the management of the electronic health record systems. IEEE Transactions on Engineering Management, 70(8), 2846–2863.

Balasubramanian, S., Shukla, V., Sethi, J. S., Islam, N., & Saloum, R. (2021). A readiness assessment framework for blockchain adoption: A healthcare case study. Technological Forecasting and Social Change, 165, 120536.

Chen, H. M., Nguyen, B. A., Yan, Y. X., & Dow, C. R. (2020). Analysis of learning behavior in an automated programming assessment environment: A code quality perspective. IEEE Access, 8, 167341–167354.

Christakis, M., & Bird, C. (2016). What developers want and need from program analysis: An empirical study. In Proceedings of the 31st IEEE/ACM International Conference on Automated Software Engineering, 332–343.

K. S. Hebbar, “MACHINE LEARNING-ASSISTED SERVICE BOUNDARY DETECTION FOR MODULARIZING LEGACY SYSTEMS,” International Journal of Applied Engineering & Technology, vol. 04, no.02, pp. 401-414, Sep. 2022, https://romanpub.com/resources/ijaet-v4-2-2022-48.pdf

Jin, S., Li, Z., B. Chen, B. Zhu, and Y. Xia. (2023). Software Code Quality Measurement: Implications from Metric Distributions. In 2023 IEEE 23rd International Conference on Software Quality, Reliability, and Security (QRS), 488–496.

Keuning, H., Heeren, B., & Jeuring, J. (2017). Code quality issues in student programs. In Proceedings of the 2017 ACM Conference on Innovation and Technology in Computer Science Education, 110–115.

Kurdi, R., Taleb-Bendiab, A., Randles, M., & Taylor, M. (2011). E-government information systems and cloud computing (Readiness and Analysis). In Proceedings of Development in E-systems Engineering, 404–409.

Li, J., He, P., Zhu, J., & Lyu, M. (2017). Software defect prediction via convolutional neural network. In Proceedings of the International Conference on Software Quality, Reliability and Security, 318–328.

Malik, S., Chadhar, M., Vatanasakdakul, S., & Chetty, M. (2021). Factors affecting the organizational adoption of blockchain technology: Extending the technology–organization–environment (TOE) framework in the Australian context. Sustainability, 13(16), 9404.

Monperrus, M. (2018). Automatic software repair: A bibliography. ACM Computing Surveys, 51(1), 1–24.

Nilashi, M., Ahmadi, H., Ahani, A., Ravangard, R., & Ibrahim, O. B. (2016). Determining the importance of hospital information system adoption factors using fuzzy analytic network process (ANP). Technological Forecasting and Social Change, 111, 244–264.

Pashchenko, I., Scandariato, R., Sabetta, A., & Massacci, F. (2021). Secure software development in the era of fluid multi-party open software and services. In Proceedings of the 2021 IEEE/ACM 43rd International Conference on Software Engineering, New Ideas and Emerging Results (ICSE-NIER), 91–95.

Řechtáčková, A., Pelánek, R., & Effenberger, T. (2024). Catalog of Code Quality Defects in Introductory Programming. In Proceedings of the 2024 on Innovation and Technology in Computer Science Education V. 1, 59–65.

Shen, H., Fang, J., & Zhao, J. (2011). Efindbugs: Effective error ranking for findbugs. In Proceedings of the 2011 4th IEEE International Conference on Software Testing, Verification and Validation, 299–308.

Most read articles by the same author(s)

<< < 3 4 5 6 7 8 9 10 11 12 > >>

International Journal of Next-Generation Engineering and Technology

The Architecture of Quality: Integrating Machine Learning, Blockchain, and Automated Analysis for the Evolution of Secure and Modular Software Systems

Abstract

Keywords

References

Most read articles by the same author(s)

Similar Articles