Bounded Generation: Software Engineering Disciplines in the Era of Generative AI
DOI:
https://doi.org/10.68050/JAMS.2026.631Keywords:
Generative Artificial Intelligence (GenAI), Productivity-Reliability Paradox (PRP), Software Delivery Stability, Comprehension Debt, Code Churn, Peer Code Review, Automation Bias, Package Hallucination, Test-Driven Development (TDD), Specification-Driven Development (SDD)Abstract
The integration of Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs) into the software development life cycle has ignited a profound transformation in how code is authored, inspected, and maintained. Early industry narratives posited that automated code generation would render traditional software engineering methodologies obsolete by commoditizing implementation. However, emerging empirical studies, multi-year repository telemetry, and controlled trials demonstrate an opposing reality characterized by the Productivity-Reliability Paradox (PRP). As generative agents lower the marginal cost of code creation to near zero, engineering organizations experience severe downstream friction: surging pull request volumes, escalating code churn, structural degradation, systemic comprehension debt, and software supply chain vulnerabilities such as package hallucinations. This paper provides an empirical investigation into the operational efficacy of foundational software engineering disciplines within AI-assisted workflows. It establishes that Peer Code Review, Test-Driven Development (TDD), modular system architecture, and formal specification governance are not historical artifacts of manual programming. Instead, they represent indispensable cognitive and deterministic guardrails required to bound non-deterministic model outputs. Rather than diminishing in value, traditional software engineering rigors must expand to transform stochastic code generation into dependable, maintainable, and production-ready enterprise software.
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Articles published in the Journal of Advanced Multidisciplinary Studies (JAMS) are licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), unless otherwise stated. Authors retain copyright of their work and grant JAMS the right of first publication.
