arXiv AI
Aug 10
Characterizing the Quality Profile of AI-Generated C++ in Production
arXiv:2608. 06640v1 Announce Type: cross Abstract: The widespread integration of AI coding assistants offers undeniable boosts to engineering velocity.
Beyond Code Generation: Reliability, Verification, and Cost Economics in the Agentic Software Development Lifecycle
The paper examines how AI coding agents are evolving beyond simple autocomplete to perform complex tasks such as repository inspection, multi-file editing, tool execution, test writing, pull request creation, and long-duration work with minimal supervision. It highlights that while these agents boost coding activity, significant bottlenecks remain in review, integration, testing, security, deployment, and production operations, and that the economics of software development are shifting toward variable token, tool, sandbox, CI, and rework costs. The authors synthesize recent research and industry data to propose four engineering concepts—Agentic SDLC Throughput Paradox, Production-Qualified Change, Verification Tax, and an Agentic SDLC Control Plane—to guide the allocation of autonomy within cost, reliability, and human-attention constraints, ultimately reframing the research focus to production-qualified value per dollar, reviewer-hour, and operational risk.
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arXiv AI
Jul 2
Cheap Code, Costly Judgment: A Case Study on Governable Agentic Software Engineering
arXiv:2607. 01087v1 Announce Type: cross Abstract: Generative AI is shifting software engineering from a practice organized around scarce implementation effort toward one organized around abundant, low-cost code production.
arXiv AI
Aug 24
SDAD: Spec-Driven Agentic Development for the AI-Native SDLC
The paper introduces Spec-Driven Agentic Development (SDAD), a framework that leverages large language models to ingest extensive functional requirement documents and repository context in a single workflow, turning specification quality into the engine for autonomous software delivery. SDAD blends disciplined upfront formalisation with rapid implementation, encompassing intent capture, machine‑readable specifications, agentic synthesis, and multi‑agent verification with human sign‑off. It positions AI‑code as a fourth production paradigm, compares it to traditional Waterfall and Agile approaches, and extends the model to team role evolution, quantitative governance metrics, and a staged migration blueprint for practical adoption.
arXiv AI
Jun 3
The Impact of Configuring Agentic AI Coding Tools on Build-vs-Buy Decisions: A Study Protocol
arXiv:2606. 03907v1 Announce Type: cross Abstract: Agentic AI coding tools write code with increasing autonomy and in doing so decide when to import a library and when to implement functionality from scratch.
arXiv Machine Learning
Jul 30
REAP: Automatic Curation of Coding Agent Benchmarks from Interactive Production Usage
arXiv:2604. 01527v4 Announce Type: replace-cross Abstract: Production deployment of AI coding agents requires fast, reproducible evaluation signals.
arXiv AI
Jul 22
Don't Blame the Large Language Model: How Agent Harness Evolution Shapes Coding Agent Quality
arXiv:2607. 03691v2 Announce Type: replace-cross Abstract: Coding agents, autonomous systems that use large language models (LLMs) to resolve software engineering tasks, rely on agent harness: a middleware layer in between a developer and a large language model that orchestrates system prompts, tool execution, context management, and iterative reasoning loops.
arXiv AI
Aug 7
Autonomous Research Agents: A Survey of AI Scientists and the Verification Gap
arXiv:2608. 05179v1 Announce Type: cross Abstract: Large language model (LLM) agents are increasingly used across the scientific research lifecycle: ideation, literature search, experiment design and execution, analysis, manuscript drafting, and review.
arXiv Machine Learning
Jun 30
SWE-INTERACT: Reimagining SWE Benchmarks as User-Driven Long-Horizon Coding Sessions
arXiv:2606. 30573v1 Announce Type: new Abstract: We introduce SWE-Interact, a new testbed for evaluating coding agents on multi-turn, interactive, user-driven software engineering tasks.
arXiv AI
Jun 4
From Prompt to Process: a Process Taxonomy and Comparative Assessment of Frameworks Supporting AI Software Development Agents
arXiv:2606. 04967v1 Announce Type: cross Abstract: AI tools for programming are no longer just autocomplete or chat assistants: they organize themselves as development frameworks, with process, roles, artifacts and verification.
arXiv AI
Sep 15
A Few Pages of Markdown: Committed AI Configuration and Lower Quality Cost after Coding-Agent Adoption
arXiv:2608.25241v2 Announce Type: replace-cross Abstract: Coding agents increase development velocity but also technical debt. Prior work reports only average effects across adopters, hiding wide dif...
arXiv AI
Aug 17
Engineering Reliable Coding Agents: Evaluating and Operating the System Around the Model
arXiv:2608. 13867v1 Announce Type: cross Abstract: AI coding agents are commonly evaluated as models but deployed as systems.
arXiv AI
Jul 24
ICAE-Bench: Evaluating Coding Agents as Interactive Project Builders
arXiv:2607. 21217v1 Announce Type: new Abstract: The recent emergence of vibe-coding workflows is changing what coding agents are expected to do.