AI-Augmented Software Engineering: A Holistic Approach to Reliability, Security, and Lifecycle Optimization
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V5I3P121Keywords:
AI-Augmented Software Engineering, Software Defect Prediction, Software Vulnerability Detection, Intelligent Testing, Observability, Lifecycle Optimization, Reliability Engineering, Secure Software DeliveryAbstract
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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