A VISUAL GRAPHIC BASED MODELING FRAMEWORK OPTI-BLEND FOR INTEGRATED CODE ANALYSIS
DOI:
https://doi.org/10.29121/shodhkosh.v7.i2s.2026.7354Keywords:
Graph-Based Modeling, Program Analysis, Static Analysis, Code Quality, Vulnerability Detection, Hybrid Program Graph, Software Engineering, Explainable AI For CodeAbstract [English]
The current software systems are becoming complicated, heterogeneous and spread out making the task of code analysis a complicated task. The tools used in the traditional program analysis work independently, the statistical analysis, dynamic analysis, inspection of dependencies, vulnerability scanning, and quality assessment are commonly done separately. The result of this fragmentation is a lack of contextual knowledge, decreased explainability and inability to find root causes of defects or vulnerabilities. In order to overcome such shortcomings, the current paper suggests the creation of Opti-Blend, a visual graph-based modeling system of integrated code analysis. Opti-Blend converts several program representations, such as Abstract Syntax Trees (AST), Control Flow Graphs (CFG), Data Flow Graphs (DFG), Program Dependence Graphs (PDG) and Call Graphs, into a Hybrid Program Graph (HPG). The framework proposes a graph fusion mechanism to be used to combine multi-view representations to a semantic model. A query layer of visualization allows people to explain the issues and investigate them through the graph paths and dependencies. The suggested system can assist in defect detection, vulnerability, and code smell identification as well as dependency risk assessment all in a single visual setting. The experimental validation on open-source repositories proves to be a better detection tool and better traceability than individual tools. Opti-Blend is a contribution to a single, understandable and extendable modeling paradigm of next-generation integrated code intelligence systems.
References
Baratchi, M., et al. (2024). Automated Machine Learning: Past, Present and Future. Artificial Intelligence Review, 57(5), Article 122. https://doi.org/10.1007/s10462-024-10741-9 DOI: https://doi.org/10.1007/s10462-024-10726-1
Chen, Z., Wu, M., Zhao, R., Guretno, F., Yan, R., and Li, X. (2020). Machine Remaining Useful Life Prediction Via an Attention-Based Deep Learning Approach. IEEE Transactions on Industrial Electronics, 68(5), 2521–2531. https://doi.org/10.1109/TIE.2020.2972444 DOI: https://doi.org/10.1109/TIE.2020.2972443
Cheng, C., Ma, G., Zhang, Y., Sun, M., Teng, F., and Ding, H. (2020). A Deep Learning-Based Remaining Useful Life Prediction Approach for Bearings. IEEE/ASME Transactions on Mechatronics, 25(3), 1243–1254. https://doi.org/10.1109/TMECH.2020.2971503 DOI: https://doi.org/10.1109/TMECH.2020.2971503
Demšar, J., and Zupan, Q. (2024). Hands-On Training about Data Clustering with Orange Data Mining Toolbox. PLoS Computational Biology, 20(10), e1012574. https://doi.org/10.1371/journal.pcbi.1012574 DOI: https://doi.org/10.1371/journal.pcbi.1012574
Francisco, O. V., and Rosaria, S. (2021). Machine Learning for Marketing on the KNIME Hub: The Development of a Live Repository for Marketing Applications. Journal of Business Research, 137, 393–410. https://doi.org/10.1016/j.jbusres.2021.08.043 DOI: https://doi.org/10.1016/j.jbusres.2021.08.036
Gaikwad, M. P. G., and Bhirud, P. A. N. (2026). AI-Powered Predictive Risk Analysis in Construction Projects Using Hybrid Machine Learning and Simulation Models. International Journal of Recent Advances in Engineering and Technology, 15(1), 1–12.
Gao, Z., Wang, C., Wu, J., Wang, Y., Jiang, W., and Dai, T. (2025). Degradation-Aware Remaining Useful Life Prediction of Industrial Robot Via Multiscale Temporal Memory Transformer Framework. Reliability Engineering and System Safety, 262, 111176. https://doi.org/10.1016/j.ress.2025.111176 DOI: https://doi.org/10.1016/j.ress.2025.111176
Graves, A. (2013). Generating Sequences with Recurrent Neural Networks. Arxiv Preprint Arxiv:1308.0850.
Hochreiter, S., and Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735 DOI: https://doi.org/10.1162/neco.1997.9.8.1735
Kim, M., Yoo, S., Son, S., Chang, S. Y., and Oh, K.-Y. (2025). Physics-Informed Deep Learning Framework for Explainable Remaining Useful Life Prediction. Engineering Applications of Artificial Intelligence, 143, 110072. https://doi.org/10.1016/j.engappai.2025.110072 DOI: https://doi.org/10.1016/j.engappai.2025.110072
Kim, Y. (2014). Convolutional Neural Networks for Sentence Classification. Arxiv Preprint arXiv:1408.5882. https://doi.org/10.3115/v1/D14-1181 DOI: https://doi.org/10.3115/v1/D14-1181
Kok, C. L., et al. (2024). A Comparative Study of AI and Low-Code Platforms for SMEs: Insights into Microsoft Power Platform, Google AutoML and Amazon SageMaker. In Proceedings of the 2024 IEEE 17th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC) (50–53). IEEE. https://doi.org/10.1109/MCSoC64144.2024.00018 DOI: https://doi.org/10.1109/MCSoC64144.2024.00018
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P. (2002). Gradient-Based Learning Applied to Document Recognition. Proceedings of the IEEE, 86(11), 2278–2324. https://doi.org/10.1109/5.726791 DOI: https://doi.org/10.1109/5.726791
Saxena, A., and Goebel, K. (2008). Turbofan Engine Degradation Simulation Data Set. NASA Prognostics Data Repository. NASA Ames Research Center.
Sutskever, I., Vinyals, O., and Le, Q. V. (2014). Sequence to Sequence Learning with Neural Networks. Arxiv Preprint ArXiv:1409.3215.
Vaswani, A., et al. (2017). Attention is All You Need. arXiv preprint arXiv:1706.03762.
Vinyals, O., Toshev, A., Bengio, S., and Erhan, D. (2015). Show and Tell: A Neural Image Caption Generator. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (3156–3164). IEEE. https://doi.org/10.1109/CVPR.2015.7298935 DOI: https://doi.org/10.1109/CVPR.2015.7298935
Wu, H., Xu, J., Wang, J., and Long, M. (2021). Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting. arXiv preprint arXiv:2106.13008.
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Prof. Tulshihar Patil, Dr. Shashank Joshi, Dr. AY Prabhakar, Prof. Akash Suryawanshi, Prof. Sudarshan Talegaonkar, Dr. Devdatta Mokashi

This work is licensed under a Creative Commons Attribution 4.0 International License.
With the licence CC-BY, authors retain the copyright, allowing anyone to download, reuse, re-print, modify, distribute, and/or copy their contribution. The work must be properly attributed to its author.
It is not necessary to ask for further permission from the author or journal board.
This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.























