CodeChat-Eval shows LLMs lose 19.2% to 69.2% functional correctness over multi-turn refinement dialogues, with largest drops on logic-level and additive changes.
The impact of llm-assistants on software developer productivity: A systematic literature review
9 Pith papers cite this work. Polarity classification is still indexing.
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Analysis of 35k AI-referencing GitHub comments shows primary use for code implementation, with evolution toward conceptual support and sustained human refinement over time.
Case study reports one staff engineer with four AI agents delivering a four-person-scoped brownfield project in half the planned time under Spec-Driven Development, with high code acceptance and major cost savings.
EditFlow reconstructs temporal developer editing flows from code changes to benchmark and optimize AI code edit recommenders so they align with natural incremental reasoning rather than static snapshots.
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.
Qualitative interview study with 22 practitioners identifies multi-level benefits, challenges, and mitigation strategies for using LLMs in software development.
The paper proposes shifting GenAI research in software engineering from narrow performance metrics to also include developer well-being, social context, and sustainable productivity.
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.
A systematic review finds research on the sustainability of LLM-generated code to be limited, fragmented, and without accepted frameworks for measurement or benchmarking.
citing papers explorer
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CodeChat-Eval: Evaluating Large Language Models in Multi-Turn Code Refinement Dialogues
CodeChat-Eval shows LLMs lose 19.2% to 69.2% functional correctness over multi-turn refinement dialogues, with largest drops on logic-level and additive changes.
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Empirical Study on the Characteristics and Evolution of AI-usage in GitHub Repositories: Evidence from Code Comments
Analysis of 35k AI-referencing GitHub comments shows primary use for code implementation, with evolution toward conceptual support and sustained human refinement over time.
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One Developer Is All You Need: A Case Study of an AI-Augmented One-Person Squad in a Brownfield Enterprise
Case study reports one staff engineer with four AI agents delivering a four-person-scoped brownfield project in half the planned time under Spec-Driven Development, with high code acceptance and major cost savings.
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EditFlow: Benchmarking and Optimizing Code Edit Recommendation Systems via Reconstruction of Developer Flows
EditFlow reconstructs temporal developer editing flows from code changes to benchmark and optimize AI code edit recommenders so they align with natural incremental reasoning rather than static snapshots.
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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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Walking the Tightrope of LLMs for Software Development: A Practitioners' Perspective
Qualitative interview study with 22 practitioners identifies multi-level benefits, challenges, and mitigation strategies for using LLMs in software development.
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At What Cost? Software Developers' Well-Being in the Age of GenAI
The paper proposes shifting GenAI research in software engineering from narrow performance metrics to also include developer well-being, social context, and sustainable productivity.
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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.
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Sustainable Code Generation Using Large Language Models: A Systematic Literature Review
A systematic review finds research on the sustainability of LLM-generated code to be limited, fragmented, and without accepted frameworks for measurement or benchmarking.