Proposes the Intelligent Computing Architecture (ICA) as a six-layer framework with dual probabilistic-deterministic planes and three Amdahl-style heuristics to unify design of LLM-based systems.
Sea change in software development: Economic and productivity analysis of the AI- powered developer lifecycle
5 Pith papers cite this work, alongside 11 external citations. Polarity classification is still indexing.
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PrivacyAkinator uses LLM-generated questions grounded in data-flow representations and a news-mined design space to help developers surface privacy decisions, yielding 47% more decisions identified in 73% less time than PRAM in a 24-person study.
Agentic AI systems are shifting software engineering from line-level code generation to delegated repository-scale execution under supervision, with SWE-bench performance rising from 1.96% to 78.4% and productivity gains of 13.6-55.8%.
Contrastive Prompt Tuning raises code accuracy on two of three tested models but produces inconsistent energy-efficiency gains that depend on model, language, and task.
Survey of 415 developers finds GenAI accelerates coding output but redistributes effort into review and verification, making net productivity gains appear spurious at current adoption levels.
citing papers explorer
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Model-Native Computing Architecture: Envisioning Future System Architecture Through the Lens of Computer Architecture
Proposes the Intelligent Computing Architecture (ICA) as a six-layer framework with dual probabilistic-deterministic planes and three Amdahl-style heuristics to unify design of LLM-based systems.
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PrivacyAkinator: Articulating Key Privacy Design Decisions by Answering LLM-Generated Multiple-choice Questions
PrivacyAkinator uses LLM-generated questions grounded in data-flow representations and a news-mined design space to help developers surface privacy decisions, yielding 47% more decisions identified in 73% less time than PRAM in a 24-person study.
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Agentic AI in the Software Development Lifecycle: Architecture, Empirical Evidence, and the Reshaping of Software Engineering
Agentic AI systems are shifting software engineering from line-level code generation to delegated repository-scale execution under supervision, with SWE-bench performance rising from 1.96% to 78.4% and productivity gains of 13.6-55.8%.
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An Initial Exploration of Contrastive Prompt Tuning to Generate Energy-Efficient Code
Contrastive Prompt Tuning raises code accuracy on two of three tested models but produces inconsistent energy-efficiency gains that depend on model, language, and task.
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The Fast and Spurious: Developer Productivity with GenAI
Survey of 415 developers finds GenAI accelerates coding output but redistributes effort into review and verification, making net productivity gains appear spurious at current adoption levels.