Coding agents increase development velocity but also technical debt. Prior work reports only average effects across adopters, hiding wide differences between teams. We introduce RAMP (Repository AI Maturity Profile), a four-level cumulative maturity model grounded in version-controlled artifacts that teams commit to configure AI tools. RAMP runs from behavioral rules and coding standards through named agent definitions to multi-agent orchestration, with observed practice concentrated in the first three levels. Across 441 repositories the levels behave as a cumulative scale, and independent human annotation reproduces RAMP's repository-level labels on 97% of a held-out sample. Adoption is cumulative, forward-only, and set-and-forget: 73.8% of artifacts are committed once and never modified. Re-estimating an existing agent-adoption panel within each stratum, agents accelerate development regardless of maturity (28-38% more commits), but quality diverges: among agent-first repositories, where the contrast is identified, those without committed AI configuration show roughly twice the increase in cognitive complexity (+53% versus +27%) and 1.7x the increase in static-analysis warnings. Because maturity is observational, correlated engineering discipline or model capability may explain part of the gap; we present these findings as hypothesis-generating and release RAMP as a reusable instrument.
Coding agents often implement changes before users have fully articulated their requirements, echoing a pattern from requirements engineering: stakeholders cannot express a constraint until part of the system exists to react to. This volatility is associated with schedule and budget overruns in traditional projects, but only at release-cycle granularity. Existing work on coding agents narrows this gap only partway: curated benchmarks fix requirements before implementation by design, and observational studies report pushback frequency without linking arrivals to the code invalidation they cause. We address this using 3,553 eligible SWE-chat sessions, coding post-implementation requirement arrivals along three dimensions and, where repository state can be replayed, linking each arrival to a proxy: deletion or replacement of prior agent-authored lines. A requirement's arrival is followed by roughly twice as much invalidation as matched non-requirement edits, robust to user-turn and net-deletion checks, though not demonstrated as causal. This burden shows no detectable decline over a session and no detected association with operation type once multiplicity is accounted for; several intervals remain wide. A controlled experiment shows delayed disclosure relocates implementation post-reveal, while advance warning produces no detected effect on overwriting. These results establish late requirement emergence as a measurable source of code invalidation.
Bo-Wen Jiang, Hao-Wei Cheng, Yu-Hong Fu et al.· 0 citations
AI coding systems are moving from autocomplete and chat toward agents that can inspect repositories, edit multiple files, run tools, write tests, open pull requests, and work for long periods with limited supervision. This capability changes the bottleneck in software delivery. Recent field studies show meaningful gains in coding activity, but newer evidence also shows that those gains attenuate sharply between writing code and shipping reliable software. Review, integration, testing, security, deployment, and production operations remain constraining stages, while the economics are shifting from predictable per-seat licensing toward variable token, tool, sandbox, CI, and rework costs. This paper synthesizes peer-reviewed software-engineering research, university studies, benchmark audits, production reports from major technology companies, developer telemetry, and cost-management evidence released primarily from 2024 through September 2026. No new model experiment is claimed; numerical findings remain attributed to their original studies. The synthesis proposes four engineering concepts: the Agentic SDLC Throughput Paradox, Production-Qualified Change (PQC), the Verification Tax, and an Agentic SDLC Control Plane that allocates autonomy subject to cost, reliability, and human-attention budgets. An evidence-based horizon then maps today's supervised agents to future policy-bounded software factories. The central research question shifts from how much code an agent can generate to how much production-qualified value an engineering system can deliver per dollar, per reviewer-hour, and per unit of operational risk.
Analysis of agentic PRs from popular GitHub repositories provides early empirical evidence on how open-source projects organize human oversight around agentic coding tools and suggests that successful integration of agent-generated contributions depends not only on advances in agent capabilities but also on the human and organizational processes that govern their use.
As LLM coding agents increasingly perform end-to-end engineering work, we lack empirical characterization of how they behave on systems-level requirements: schema design, async orchestration, configuration correctness, and retrieval-filtering trade-offs. We present a case study of one such agent implementing a multi-component data system against a detailed pre-existing specification. Storage technologies, schema, entity-resolution algorithm, and retrieval-filtering strategy were fixed in advance; the agent autonomy was in the implementation, in diagnosing and fixing defects it introduced, and in interaction-design choices left open. Over a single session, we catalog five such defects, categorized by constraint violated and detection method. We further evaluate, on the public HotpotQA benchmark, the one retrieval trade-off specified in that architecture: restricting candidates to a graph-identified entity set before ranking versus unfiltered search. We substitute the benchmark gold evidence labels for entity identification, since we lacked LLM access to run that stage, and report standard recall rather than the benchmark own accuracy metrics. Across retrieval budgets from 1 to 10 and 100 questions against a pooled corpus of 2994 paragraphs, filtered recall reaches its ceiling by a budget of 3, expected once candidates are restricted to the gold paragraphs themselves, while unfiltered search recovers all required evidence only 69 percent of the time even at a budget of 10, a gap that holds at every budget tested, with sign test p less than 0.0001. We close with a discussion of where the agent autonomy succeeded versus required correction, including one instance where a claimed performance fix was never re-measured on the regression that motivated it.
This work introduces multi-agent from-scratch evaluation benchmark, MSEval, evaluating multi-agent coding on real-world tasks, and establishes a rigorous, reproducible standard for measuring how multi-agent teams actually build software.
Yanyu Ren, Yu Bai, Xizheng Wang et al.· arXiv.org· 0 citations
SWE Refactor Bench is introduced, a benchmark comprising 20 whole-repository migrations, covering 4 kinds of technical debt, and SWE Refactor Bench is positioned as a rigorous testbed for developing coding agents for reliable whole-repository migrations.
Deyao Hong, Y. Chi, Wenyi Li et al.· 0 citations
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