Integrating Observability, Defect Prediction, and Decision Intelligence for Reliable AI-Driven Software Systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it