Integrating Observability, Defect Prediction, and Decision Intelligence for Reliable AI-Driven Software Systems
Bibliographic record
Abstract
AI-driven software systems have expanded the operational surface area of modern platforms by coupling conventional application logic with data pipelines, machine learning components, and continuously changing runtime environments. This expansion makes reliability a moving target. Traditional quality assurance and post-deployment monitoring remain necessary, but they are no longer sufficient when failures emerge from interactions among code defects, model drift, infrastructure volatility, feature-store inconsistencies, and delayed operational response. This paper develops an integrated conceptual framework that unifies observability, software defect prediction, and decision intelligence into a single reliability architecture for AI-driven software systems. The proposed perspective argues that these capabilities should not be treated as isolated disciplines. Observability provides high-fidelity runtime evidence, defect prediction offers anticipatory risk estimation before failures become customer-visible, and decision intelligence converts technical signals into prioritized actions, governance routines, and architecture-level trade-off decisions. Drawing on literature from AIOps, MLOps, software quality engineering, testing, trustworthy AI, and architecture-centric governance, the paper synthesizes current knowledge, identifies fragmentation across the lifecycle, and proposes a layered operating model spanning development, deployment, operations, and continuous improvement. The manuscript also outlines adoption patterns, organizational prerequisites, and research challenges relevant to enterprise-scale implementation. The resulting framework is intended to support more reliable, auditable, and adaptive AI-enabled software delivery in complex socio-technical environments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".