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#DORA

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Arquitectura y Construcción Traducción verificada automáticamente

The End of Code Review: Coding Agents Supersede Human Inspection

An arXiv paper (cs.SE) by Martin Monperrus arguing a radical thesis for the SDLC: coding agents have crossed a threshold of capability such that **human code review is no longer a necessary component** of a quality pipeline. Two claims: (1) autonomous LLM-based systems achieve all the goals of review (defect detection, quality, compliance) at lower cost and higher throughput; (2) the hybrid model "the agent writes, the human reviews" is untenable — it does not ensure real quality and does not scale with AI velocity, creating a "false sense of security". Monperrus contrasts inspection de Fagan (1976) with a **multi-agent adversarial verification pipeline** (generator agent + independent reviewer agents + tests/formal methods + vote-based consensus). The human refocuses on the spec, architectural trade-offs, approval of critical domains, and edge cases. Recommendations: pilot first on low-risk components, measure agent vs. human, make rejection decisions explicit.

#code review#code review#inspection de Fagan

Martin Monperrus

Transformación y Adopción Traducción verificada automáticamente

The AI-native SDLC is paying off: 19% more PRs and 2–3 hours saved per developer per week

Atlassian data study (Inside Atlassian) measuring the actual return of an **AI-native SDLC** powered by **Rovo Dev**. Across 3,400 repositories from 2,500 customers (a quasi-experiment with propensity-score matching), adopting repositories merge **19% more PRs per month**; up to **37-51%** on low/medium-activity repositories and **59-87%** when **3 to 5 members** of the team adopt the tool. On the efficiency side, developers save **2-3 h/week** (≈10% of the 24 hours devoted to coding and review), i.e. 20-30 hours/week reinvested for a team of 10. The thesis: resolve Solow's (1987) "productivity paradox" by shifting from **usage metrics** (tokens) to **impact metrics** (throughput, time saved, failure rate, satisfaction). Recommendation: start with a **team** (not an individual) and measure 2-3 months later.

#AI-native SDLC#Rovo Dev#coding agents

Robbie Geoghegan · Fan Jiang (Atlassian)