#ai-assisted-development News & Analysis

4 articles tagged with #ai-assisted-development. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

4 articles

AI × CryptoNeutralarXiv – CS AI · Jun 197/10

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Secure Coding Drift in LLM-Assisted Post-Quantum Cryptography Development: A Gamified Fix

Researchers identify 'Secure Coding Drift,' a vulnerability where developers gradually adopt insecure practices when relying on LLM-generated code for post-quantum cryptography implementation. The paper proposes a gamified framework that transforms LLMs into active security partners through adversarial evaluation and behavioral feedback to mitigate this socio-technical risk.

AINeutralarXiv – CS AI · Jun 96/10

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Structuring agentic AI for HPC code modernization

Researchers successfully modernized NMAP-RKPM, a 60,000-line Fortran physics simulation engine, from single-threaded MPI to parallel C++ using a structured agentic AI approach. Rather than relying on LLMs alone, the team developed a 'hand-holding' methodology combining manual examples, continuous buildability checks, and scoped sessions that proved highly effective for legacy code transformation.

AIBearisharXiv – CS AI · Jun 56/10

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Human Oversight and Overload: Two Hidden and Costly Burdens of AI-Assisted Software Engineering

A research paper examines two overlooked burdens in AI-assisted software engineering: the mandatory human oversight required to validate AI-generated code and the cognitive overload developers experience from excessive AI suggestions. The findings highlight that while AI tools boost productivity, they create hidden costs that organizations must address to prevent developer burnout and maintain code quality.

AINeutralarXiv – CS AI · Apr 156/10

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Modeling Co-Pilots for Text-to-Model Translation

Researchers introduce Text2Model and Text2Zinc, frameworks that use large language models to translate natural language descriptions into formal optimization and satisfaction models. The work represents the first unified approach combining both problem types with a solver-agnostic architecture, though experiments reveal LLMs remain imperfect at this task despite showing competitive performance.