AI-Augmented Software Engineering: A Holistic Approach to Reliability, Security, and Lifecycle Optimization
Bibliographic record
Abstract
Software engineering has entered a period in which reliability, security, delivery speed, and operational efficiency can no longer be optimized in isolation. Modern software systems are distributed, continuously deployed, highly instrumented, and increasingly dependent on data intensive services, cloud platforms, and automated decision logic. In this setting, artificial intelligence is being applied not only to code generation but also to defect prediction, vulnerability analysis, observability, testing, and architectural governance. Existing studies, however, often examine these capabilities as disconnected point solutions. Foundational reviews of defect prediction show the long standing value of data driven quality estimation [1], while recent domain specific implementations demonstrate the practical importance of secure microservice design in regulated environments [2]. At the same time, survey work on large language models for software engineering highlights both the breadth of automation opportunities and the substantial risks associated with hallucination, over trust, and weak evaluation [3]. Empirical defect prediction work continues to show that model choice matters for actionable quality management [4], and software security surveys indicate that deep learning based vulnerability analysis has matured into a serious engineering capability rather than a purely experimental technique [5]. This paper proposes a holistic research framework for AI augmented software engineering that integrates reliability engineering, software security, and lifecycle optimization into a unified operating model. Rather than treating intelligence as a late stage assistant layered on top of development, the paper argues for embedding AI across planning, coding, testing, release, monitoring, and feedback loops. The framework organizes evidence and methods into three tightly coupled layers: predictive reliability, security aware reasoning, and lifecycle optimization. For each layer, the paper synthesizes prior research, defines architectural building blocks, identifies measurable outcomes, and outlines an evaluation agenda appropriate for industrial environments. The contribution is therefore twofold: first, a rigorous synthesis of relevant streams of literature and practice; second, a conceptual blueprint for how enterprises can align AI driven software engineering with measurable operational quality. The resulting perspective is intended to support researchers designing next generation quality engineering methods and practitioners building dependable, secure, and economically sustainable delivery systems.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".