RubberDuckBench shows top AI models score around 68% on real GitHub coding questions, rarely answer completely correctly, and hallucinate in 58% of responses on average.
How much does AI impact development speed? An enterprise-based randomized controlled trial
5 Pith papers cite this work. Polarity classification is still indexing.
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Multi-agent LLM teams outperform human teams in creativity (d=1.50) across tasks by producing more novel ideas, with distinct semantic exploration patterns predicting success for each group.
Meta-analysis of 23 studies shows moderate productivity gains from GenAI coding assistants (Hedges' g=0.33) but no significant effect on learning (g=0.14).
The Productivity-Reliability Paradox arises because AI code generators produce variable output while developers lack sufficient specification discipline, making governance models focused on specifications the binding constraint rather than model improvements.
Agentic Agile-V uses Agile-V as backbone and a Specify-Constrain-Orchestrate-Prove-Evolve-Verify loop to convert AI agent conversations into traceable engineering artifacts with acceptance evidence.
citing papers explorer
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RubberDuckBench: A Benchmark for AI Coding Assistants
RubberDuckBench shows top AI models score around 68% on real GitHub coding questions, rarely answer completely correctly, and hallucinate in 58% of responses on average.
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Multi-agent AI systems outperform human teams in creativity
Multi-agent LLM teams outperform human teams in creativity (d=1.50) across tasks by producing more novel ideas, with distinct semantic exploration patterns predicting success for each group.
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A meta-analysis of the effect of generative AI on productivity and learning in programming
Meta-analysis of 23 studies shows moderate productivity gains from GenAI coding assistants (Hedges' g=0.33) but no significant effect on learning (g=0.14).
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The Productivity-Reliability Paradox: Specification-Driven Governance for AI-Augmented Software Development
The Productivity-Reliability Paradox arises because AI code generators produce variable output while developers lack sufficient specification discipline, making governance models focused on specifications the binding constraint rather than model improvements.
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Agentic Agile-V: From Vibe Coding to Verified Engineering in Software and Hardware Development
Agentic Agile-V uses Agile-V as backbone and a Specify-Constrain-Orchestrate-Prove-Evolve-Verify loop to convert AI agent conversations into traceable engineering artifacts with acceptance evidence.