Failure Root Cause Taxonomy

Updated 21 October 2025

Failure root cause taxonomy is a formal classification that distinguishes observable symptoms from deeper causal factors using logic-based, statistical, and counterfactual analyses.
It enables fault localization, efficient mitigation, and post-mortem analysis in domains ranging from IT infrastructures to agentic LLM-based platforms.
Methodologies include graph-based causal analysis, machine learning inference, and knowledge-based reasoning to quantify failure impact using standardized metrics.

A failure root cause taxonomy is a formal, structured classification that organizes and explicates the underlying technical reasons for system failures across modern computing environments, ranging from traditional IT infrastructures and software engineering to cloud-native microservices, industrial systems, and agentic LLM-based platforms. These taxonomies serve as indispensable frameworks for reliable fault localization, efficient mitigation, and rigorous post-mortem analysis. They encode the causal structure of failures—not only at the level of observed symptoms (e.g., anomalies in logs or metrics) but also at the level of deeper latent factors, architectural dependencies, and the propagation pathways of faults both within and across system layers.

1. Foundational Principles of Failure Root Cause Taxonomy

At its core, a failure root cause taxonomy distinguishes between immediate symptoms and underlying explanatory mechanisms. The most advanced taxonomies apply rigorous causal inference, abductive reasoning, or counterfactual analysis to map observed failures to plausible root causes.

Early approaches model system dependencies with logic-based graphs: for example, Markov Logic Networks (MLNs) where logical formulas such as specificallyDependsOn(x,y) ∧ unavailable(y) ⇒ unavailable(x) encode causal propagation, and root cause identification becomes abductive inference over these weighted networks (Schoenfisch et al., 2015).
Advanced statistical frameworks, such as hybrid causal discovery using Hawkes processes and conditional independence tests, systematically reconstruct influence graphs in domains like telecom network management, with downstream application of influence maximization for root cause ranking (Zhang et al., 2021).
Recent methodologies in dynamic and partially observed systems synthesize structural causal models (SCMs), counterfactual reasoning, and Shapley-value ranking to provide both temporal localization and quantitative attribution of failure responsibility (Weilbach et al., 12 Jun 2024, Gong et al., 8 Jul 2024, Trilla et al., 10 Jul 2024).

In all approaches, three universal dimensions appear: the necessity for observable evidence (metrics, logs, traces, configuration), formal representation of system dependencies or risk, and explicit or implicit mechanisms for mapping from symptoms to root causes.

2. Taxonomic Structures and Classification Schemes

The schema for root cause classification is defined by both the target domain and the diagnostic methodology. The taxonomy can be unidimensional (focusing on types of faults) or multidimensional (organizational layers, timescales, or propagation pathways).

Examples of Prominent Taxonomic Systems

Domain/Method	Taxonomic Axes and Classes
IT Infrastructure (MLN)	Causes: dependency violation (specific/generic), redundancy, risk propagation.
Field Failures (Software)	Fault origin: insufficient testing vs. field-intrinsic (irreproducible condition, unknown application/env., CE)
Bugs (Bug Report Analysis)	Root cause classes: Configuration, Network, Database, GUI, Performance, Permission/Deprecation, Security, Test code
Deep Learning Systems	Categories: Model architecture, Tensor/Input, Training, GPU Usage, API usage—each further subdivided (Humbatova et al., 2019)
Multi-modal Microservices RCA	Data/modalities: Metrics, Traces, Logs; Failure types: value/timing/system failures; Representation: causality graph
Agentic Platform Systems	Level: Agent-level, Workflow-level, Platform-level (Ma et al., 28 Sep 2025)
Industrial/Knowledge Graphs	Fault propagation from data-driven variable contributions over a priori entity graphs (device/stream/state links)

Taxonomies may further encode detectability (signaled, unhandled, silent, self-healed (Gazzola et al., 2017)), failure severity, or the level of human vs. system awareness.

3. Methodological Approaches to Root Cause Attribution

Three technical threads dominate the methodologies deployed for taxonomic root cause analysis.

A. Graph-based Causal Analysis

Dependency and influence graphs are constructed from either domain knowledge (e.g., MLN first-order logic) or from statistical learning (e.g., causality from Hawkes process intensities, conditional independence, or Granger causality applied to metrics/traces).
Edge weights encode either deterministic risk assignments, probabilistic intensities, or learned propagative influence (e.g., skip-gram embeddings over alarm context graphs).
Root causes are localized by influence maximization, random walk algorithms, or evidence chain extraction (as in KylinRCA’s cross-modal GAT over full-stack observability graphs (Hou, 8 Sep 2025)).

B. Machine Learning and Data-centric Root Cause Inference

NLP-based pipeline: Unsupervised and supervised techniques transform unstructured logs into event abstractions (via log parsing), then cluster or classify failures based on event frequency, context, and heuristic rules (e.g., NCChecker (Gao et al., 5 May 2024), LogGrouper (Abbas et al., 2023)).
ML classifiers—Logistic Regression, Random Forests, deep neural networks—are trained with engineered or automatically abstracted features, using strategies such as TF-IDF vectorization, BERT embeddings, and over-/under-sampling for data imbalance.
Semi-supervised Positive-Unlabeled (PU) learning is leveraged to handle noisy, weakly labeled failure windows, particularly for rare or evolving root causes (LogRCA (Wittkopp et al., 22 May 2024), LogLAB (Wittkopp et al., 2023)).

C. Counterfactual and Knowledge-based Reasoning

Counterfactual queries (e.g., “Would the failure have occurred had subsystem $j$ behaved normally at time $t$ ?”) are executed by abduction–action–prediction over structural causal models, combining simulation and repair strategies to validate root cause candidates (Weilbach et al., 12 Jun 2024, Trilla et al., 10 Jul 2024, Ma et al., 28 Sep 2025).
Knowledge graph frameworks formalize entity, device, and attribute relationships, combining domain expert triples and learned fault propagation, with ripple or attenuation mechanisms simulating plausible causal spreads (Root-KGD (Chen et al., 19 Jun 2024)).
LLMs are increasingly used to diagnose failure and match observed evidence against taxonomic definitions, though reliability is still modest—e.g., 33.6% accuracy in automated agentic platform RCA (Ma et al., 28 Sep 2025).

4. Practical Impact and Application Domains

These taxonomies and methodologies translate into diverse practical settings:

IT and Telecom: MLN-based tools and hybrid causal graphs provide scalable, interpretable diagnosis, integrating both explicit dependency knowledge and empirical risk data for root cause identification and incident response (Schoenfisch et al., 2015, Zhang et al., 2021).
Software Engineering: Taxonomies built from field and bug reports reveal that combinatorial explosion, external environment conditions, and silent failures dominate undetected field faults, with direct implications for runtime verification and test oracle development (Gazzola et al., 2017, Catolino et al., 2019).
Machine Learning Systems: Layered taxonomies for deep learning distinguish model architecture, input, training, and operational errors, underscoring the need for domain-specific diagnostic kernels and better mutation operators (Humbatova et al., 2019).
Cloud, Microservices, and DevOps: Cross-modal RCA frameworks integrate metrics, logs, and traces, leveraging graph neural networks (GNNs), hybrid causal discovery, and attention-based modal fusion for scalable, explainable diagnosis (Wang et al., 23 Jul 2024, Hou, 8 Sep 2025).
Industrial and Physical Systems: Counterfactual-based and knowledge graph-rooted root cause diagnosis enable fine-grained, online analysis of process faults and physical device interactions in manufacturing and process control (Chen et al., 19 Jun 2024).
Agentic and LLM-Driven Platforms: Taxonomies for platform-orchestrated multi-agent systems explicitly classify agent-level, workflow-level, and platform-level root causes, validated via counterfactual trajectory repair (Ma et al., 28 Sep 2025).

5. Challenges, Limitations, and Future Directions

The literature highlights persistent challenges that directly affect the completeness and expressiveness of root cause taxonomies:

Partial Observability and Unobserved Confounders: The presence of missing nodes and latent malfunctions can fundamentally limit RCA accuracy (Gong et al., 8 Jul 2024). Contemporary frameworks (e.g., PORCA) magnify structural causal models to incorporate hidden confounders and employ heterogeneity-aware reweighting to support more faithful taxonomic inference.
Field Intrinsic and Silent Failures: Large classes of faults are inherently undetectable at design time due to combinatorial explosion or unpredictability of operational environment, implying that taxonomy-driven RCA must incorporate runtime analysis, anomaly detection, and adaptation (Gazzola et al., 2017).
Explainability and Human-in-the-Loop: State-of-the-art frameworks (KylinRCA (Hou, 8 Sep 2025), Root-KGD (Chen et al., 19 Jun 2024)) emphasize auditable evidence chains, mask-based explanation, and transparent aggregation of diagnostic information. A key trend is integration with operator expertise for iterative refinement of the taxonomy and improved actionable guidance.
Scalability and Multimodality: Processing PB-level observability data in large-scale, evolving systems demands incremental, real-time, and resource-efficient RCA techniques. Graph learning approaches with type/relationship attention and modal fusion are preferred.
Agentic and LLM-Driven Complexity: Multi-agent coordination, planning, and tool invocation failures reveal unique taxonomic requirements. Even with taxonomy guidance, automated RCA on multi-agent logs remains below 35% accuracy, motivating further research in counterfactual reasoning, fine-grained annotation, and agent workflow modeling (Ma et al., 28 Sep 2025).

6. Theoretical and Empirical Metrics for Evaluation

Root cause taxonomy research is grounded in measurable evaluation protocols:

Standard machine learning metrics (precision, recall, F1, AUC-ROC), clustering metrics (Silhouette Coefficient, Calinski-Harabasz), and ranking metrics (Mean Average Rank) are used to quantify taxonomy-driven diagnosis (Wang et al., 23 Jul 2024, Abbas et al., 2023).
Causal inference strength is often computed as change in prediction error or failure probability under intervention (e.g., C(i→j, t) = [Loss(j, t|perturb(i)) – Loss(j, t|orig)] / Loss(j, t|orig); Shapley-value ranking for time-resolved impact (Weilbach et al., 12 Jun 2024, Hou, 8 Sep 2025)).
For multi-modal and industrial settings, cosine similarity between data-driven fault feature vectors and knowledge graph-propagated simulated features formalizes root score computation (Chen et al., 19 Jun 2024).

A plausible implication is that advances in formal evaluation and benchmarking, especially those that capture causal relevance and propagation fidelity, are integral to future taxonomy robustness.

7. Outlook and Research Trajectories

The future of failure root cause taxonomy is shaped by several active threads:

Dynamic, Continual, and Online Taxonomy Adaptation: The move towards continual learning, automated adaptation to unobserved system changes, and online RCA is defining next-generation observability tooling.
Integration of Explainable AI, LLMs, and Knowledge Graphs: Enabling RCA systems that not only classify but also explain, summarize, and suggest actionable repairs—possibly in collaboration with LLMs guided by formal taxonomies—is a growing trend.
Standardization and Cross-domain Benchmarking: The development of community-shared datasets (e.g., AgentFail (Ma et al., 28 Sep 2025)), open taxonomies, and reproducible benchmarks underpins progress in both academia and industry.
Multi-Layer and Cross-Modal Analysis: RCA platforms increasingly operate across stack layers, integrating signals from metrics, logs, traces, and domain knowledge graphs for holistic causal localization.

In summary, the field is moving toward comprehensive, explainable, and adaptive taxonomies that merge causal inference, data-driven learning, and human-centric refinements to achieve robust root cause analysis for complex, evolving systems.