Cross-Source Error Triage from Logs and Memory Dump Signatures: A Unified Diagnostic Framework
DOI:
https://doi.org/10.66238/xrbnm059Keywords:
software diagnostics, error triage, log analysis, memory dumps, data fusionAbstract
The increasing complexity of modern distributed software systems necessitates advanced automated diagnostic techniques to maintain high availability and reliability. When critical system failures occur, engineers typically rely on diagnostic artifacts such as execution logs and memory dumps to identify root causes. However, traditional error triage processes isolate these artifacts, leading to incomplete diagnostic contexts and prolonged mean time to resolution. This paper proposes a novel framework for cross-source error triage that synergistically analyzes unstructured application logs and structured memory dump signatures. By extracting temporal event sequences from logs and static execution states from memory dumps, the proposed methodology constructs a unified multi-modal representation of system failures. We develop a robust alignment algorithm that bridges the semantic gap between continuous log streams and discrete memory snapshots. Through extensive theoretical analysis and empirical evaluation on a comprehensive dataset of software faults, we demonstrate that the cross-source approach significantly outperforms single-source baselines in fault classification and root cause localization. The findings reveal that integrating contextual logs with deterministic stack traces reduces ambiguity in error diagnosis, particularly in concurrent and distributed computing environments. This research contributes a foundational architecture for next-generation automated software diagnostics, offering scalable solutions for enterprise-grade applications.
References
1. Huang, D., Song, L., Chen, Y., He, S., Wang, R., Shen, H., & Chu, W. (2026). AnExplainable AI Engineering Framework for Claims-Only First-Stage Provider Audit Triage Using SHAP-Guided Hybrid Retrieval-Augmented Generation. Machine Learning and Knowledge Extraction, 8(9), 279.
2. Fan, J., Rosu, P., Wang, A. T., Carin, L., & Cheng, X. (2026). In context semi-supervised learning. In Proceedings of the International Conference on Learning Representations. https://openreview.net/forum?id=lqrpmqrTnH
3. Lyu, S., Mao, S., & Ma, Y. (2025). Unified Long Video Inpainting and Outpainting via Overlapping High-Order Co-Denoising. arXiv preprint arXiv:2511.03272.
4. Yao, Z., Hawi, P., Aitharaju, V., Mahishi, J., & Ghanem, R. (2026). PINNA: Physics‐Informed Neural Networks Architecture for Implicit Governing Physics With Interpretability. International Journal for Numerical Methods in Engineering, 127(17), e70399.
5. Sang, Y. (2026, March). Adaptive Quantization Strategies for Robust ML Inference Under Distribution Shift. In Proceedings of the 2026 5th International Conference on Cyber Security, Artificial Intelligence and Digital Economy (pp. 362-368).
6. Ghemawat, S., Gobioff, H., & Leung, S.-T. (2003). The Google file system. In Proceedings of SOSP.
7. Xiaoteng Ma, Yiqin Yang, Hao Hu, Qihan Liu, Jun Yang, Chongjie Zhang, Qianchuan Zhao, and
Bin Liang. Offline reinforcement learning with value-based episodic memory. arXiv preprint
arXiv:2110.09796, 2021b.
8. Guo, F., Xu, K., Zhang, Z., Zhou, H., Chen, G., Hu, J., ... & Mo, H. (2025). Battery SOH prediction under different conditions via MBLSTM and iTransformer with anomaly detection and explainability. IEEE Open Journal of the Computer Society.
9. Sun, F., He, S., Wang, R., Ke, L., Shen, H., & Liao, Q. (2026). Modeling Structural Deviation in 10-K Risk Factors: A Semantic Anomaly Detection and Explainable AI Approach. Risks, 14(4), 87.
10. Li, Y. (2026, June). AI-augmented automated triage for system error reporting using log and memory dump analysis. In 2026 7th International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE) (pp. 572-577). IEEE.
11. Qiu, M., Wu, L., & Wang, S. (2026). CyberDQFormer: A Knowledge Graph-Enhanced Transformer Framework for Security-Aware Data Quality Anomaly Detection and Autonomous Repair Against Network Attacks. Fundamental Scientific Reports in Multidisciplinary Areas, 2(3), 34-52.
12. Guo, F., Tao, S., Zhang, J., Zhou, H., Ji, J. J., Dong, D., & Mo, H. (2026). Frequency Spectral Prior Guided Graph Learning for Degradation Decoupling in Reliable Battery SOH Estimation. Green Energy and Intelligent Transportation, 100446.
13. Wang, J., Xu, Y., Li, Y., Khargonekar, P., & Faruque, M. A. A. (2026). CRUISE: Vision-Language Model-Guided Uncertainty-Aware Cross-Modal Sensor Fusion for Robust Autonomous Driving. arXiv preprint arXiv:2608.09202.
14. Zhang, J., Luo, H., Yang, H., Xu, X., Wu, Q., Shi, Y., ... & Wang, J. (2023). Neuraldome: A neural modeling pipeline on multi-view human-object interactions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 8834-8845).
15. Li, Q., Luo, T., Jiang, M., Jiang, Z., Hou, C., & Li, F. (2025, April). Semi-supervised multi-view multi-label learning with view-specific transformer and enhanced pseudo-label. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 39, No. 17, pp. 18430-18438).
16. Lu, L., Su, J., Wang, J., & Deng, X. (2026). AMSPINN: anisotropic and multiscale cross-attention physics-inspired neural networks for turbulence prediction. Engineering Applications of Computational Fluid Mechanics, 20(1), 2628951.
17. Wu, J., Chen, S., Heo, I., Gutfraind, S., Liu, S., Li, C., ... & Sharps, M. (2025). Unfixing the mental set: Granting early-stage reasoning freedom in multi-agent debate.
18. Lin, Y. (2024). Design of urban road fault detection system based on artificial neural network and deep learning. Frontiers in neuroscience, 18, 1369832.
19. Li, Q., Luo, T., Jiang, M., Liao, J., & Jiang, Z. (2024, October). Deep incomplete multi-view network semi-supervised multi-label learning with unbiased loss. In Proceedings of the 32nd ACM International Conference on Multimedia (pp. 9048-9056).
20. Sigelman, B. H., Barroso, L. A., Burrows, M., Stephenson, P., Plakal, M., Beaver, D., et al. (2010). Dapper, a large-scale distributed systems tracing infrastructure. Technical Report, Google.
21. Mo Z. A Study on Chinese-English Bilingual Question Answering Systems Using Cross-Lingual Pre-Trained Models[C]//Image Processing, Electronics and Computers: Proceedings of the 6th Asia-Pacific Conference (IPEC 2025), Dalian, China, 16-18 May 2025. 1 Oliver's Yard, 55 City Road, London, EC1Y 1SP: SAGE Publications, 2025: 1715-1724.
22. Liu, Y., Cai, Q., Wang, C., Yang, J., Fan, H., Dong, J., & Chen, S. (2024). Geometry-enhanced attentive multi-view stereo for challenging matching scenarios. IEEE Transactions on Circuits and Systems for Video Technology, 34(8), 7401-7416.
23. Reimers, N., & Gurevych, I. (2019). Sentence-BERT: Sentence embeddings using Siamese BERT-networks. In Proceedings of EMNLP-IJCNLP.
24. Sun, L., Zhang, Z., Zhang, J., Wang, F., Du, Y., Su, S., & Yu, P. S. (2020, November). Perfect: A hyperbolic embedding for joint user and community alignment. In 2020 IEEE International Conference on Data Mining (ICDM) (pp. 501-510). IEEE.
25. Yu, Z., Dai, L., Xu, S., Gao, S., & Ho, C. P. (2023). Fast bellman updates for wasserstein distributionally robust mdps. Advances in Neural Information Processing Systems, 36, 30554-30578.
26. Zhang, J., & Tang, H. (2025). Resolving Task Objective Conflicts in Unified Model via Task-Aware Mixture-of-Experts. arXiv preprint arXiv:2506.03591.
27. Liu, Chenchen, Fuqiang Liu, and Hai Li. "Brain-inspired computing accelerated by memristor technology." In Proceedings of the 4th ACM International Conference on Nanoscale Computing and Communication, pp. 1-6. 2017.
28. Wang, Y., He, S., Chen, G., Chen, Y., & Jiang, D. (2022, December). XLM-D: Decorate cross-lingual pre-training model as non-autoregressive neural machine translation. In Proceedings of the 2022 conference on empirical methods in natural language processing (pp. 6934-6946).
29. Fan, J., Gao, B., Jin, H., & Jiang, L. (2022). UCC: Uncertainty guided cross-head co-training for semi-supervised semantic segmentation. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 9937–9946). IEEE. https://doi.org/10.1109/CVPR52688.2022.00971
30. Huang, Y., Roth, K., Bouniot, Q., Xu, W., & Akata, Z. (2026). Dissecting Multimodal In-Context Learning: Modality Asymmetries and Circuit Dynamics in modern Transformers. arXiv preprint arXiv:2601.20796.
31. Meng, X., & Lin, J. (2026). AWCRL: Adaptive Weakly Supervised Cross-Modal Reliability Learning for resource-constrained proxy-state recognition. Pattern Recognition, 114998.
32. Meng, X., & Lin, J. (2026). CARTN: Counterfactual Audit and Repair for Trust-Aware Multi-Signal Streaming LiDAR Segmentation. Information Fusion, 104809.
33. Chen, L., Wang, J., Mortlock, T., Khargonekar, P., & Al Faruque, M. A. (2025, June). Hyperdimensional uncertainty quantification for multimodal uncertainty fusion in autonomous vehicles perception. In 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 22306-22316). IEEE.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Sonia So (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in the International Journal of Machine Learning and Engineering (IJMLE) are licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
This license permits unrestricted use, distribution, reproduction, adaptation, and commercial use in any medium or format, provided the original author(s) and the source are properly credited.
Authors retain the copyright of their work while granting the journal the right of first publication.
To view a copy of this license, please visit:
https://creativecommons.org/licenses/by/4.0/
