Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.11179

Who Pays the Review Cost? Triage, Fairness, and Accountability in AI-authored Pull Requests

Md Shamimur Rahman·Khairul Alam·Banani Roy·Chanchal K. Roy
View original source ↗Read source document ↗Export metadata ↓

Abstract

AI coding agents are moving from local code assistance into pull-based workflows, where generated contributions must be reviewed, explained, and maintained within existing project norms. Although recent work has begun to characterize AI-authored pull requests (AIPRs), less is known about how reviewers govern their entry into review, how AI authorship reshapes credibility and fairness, and what intake mechanisms protect review sustainability. We report a mixed-method questionnaire survey of 239 practitioners from 31 countries with code-review experience and varying exposure to AIPRs. In the scenarios and self-reports elicited by the survey, AI authorship was not a categorical rejection signal. Instead, respondents described review effort as conditional on whether an AIPR arrived as an accountable contribution, with bounded scope, project-grounded rationale, validation beyond Continuous Integration (CI), contributor responsiveness, and identifiable post-merge ownership. This conditional logic extended to newcomer AIPRs, where respondents emphasized visible participation in the current review process over profile-level reputation alone. Qualitative responses further described shifts in mentoring, scrutiny, deferral, and routing when human stewardship was difficult to observe. We conceptualize missing rationale, validation, and ownership as reviewability debt, the work reviewers must absorb when generated code lacks sufficient human grounding. These findings reframe AIPR governance around making human judgment observable before generated contributions consume scarce reviewer attention.

Explore related subjects

cs.SE

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Md Shamimur Rahman, Khairul Alam, Banani Roy, Chanchal K. Roy. 2026-10-08. Who Pays the Review Cost? Triage, Fairness, and Accountability in AI-authored Pull Requests. https://arxiv.org/abs/2610.11179

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Bistable by Construction: Wall-Clock-Calibrated State Monitors Have No Moment-Detection Regime at Agent Cadence

Runtime monitors for autonomous agents commonly threshold an accumulated internal state - a behavioural baseline, a drift statistic, or, in our prior work, a modelled affective state. We previously reported a State Saturation Trap: threshold-on-state triggers over a continuous affect engine become near-constant alarms on SWE-bench debugging agents (Modgil 2026). A post-release audit found the engine received dt=0 between actions, so its exponential decay never operated: the published trap is a pure-accumulator result. We correct the record (erratum, v2) and treat the flaw as an experiment. The key variable it exposes is whether a monitor's dynamics are calibrated in sample time (per observation, as in CUSUM) or wall-clock time (half-lives in seconds, as in affect models and EMA baselines). On fixed-rate streams these coincide; on agent streams, where inter-action time varies by orders of magnitude, they do not. A pre-registered sweep over uniform intervals (dt in {0..600}s) on 20 trajectories shows the wall-clock level trigger has two regimes: at dt<=1s a constant alarm (20/20; median 18 firings); at dt>=60s silent. Every critical dt lies in (1,30]s. A scriptable gpt-5.4-mini agent loop measures latency at median 1.53s (p90 2.33s), inside the trap regime, vindicating the empirical finding under a corrected mechanism. The structure is a property of the calibration class, not the engine: a minimal wall-clock accumulator over the raw error stream reproduces the same cliff, while a sample-time CUSUM over the identical stream is exactly dt-invariant (20/20). A rising-edge trigger with hysteresis fires 0-3 times per trajectory in every condition. We conclude that wall-clock-calibrated leaky-integrator monitors admit no regime in which they act as moment detectors on agent streams; transition detection escapes the trap at every cadence, but does not recover human intervention timing.

cs.SE↗
arXiv2026

The Spec Growth Engine: Spec-Anchored, Code-Coupled, Drift-Enforced Architecture for AI-Assisted Software Development

AI coding agents dramatically accelerate implementation speed but introduce two structural failure modes that existing spec-driven approaches do not fully solve: (1) context explosion -- the agent must reason over an entire repository at once, degrading output quality as the context window fills; and (2) silent spec-code drift -- code evolves, the specification does not, and the divergence becomes invisible until it is costly to repair. We present the Spec Growth Engine, a lightweight framework that addresses both failure modes through a machine-readable spec graph whose nodes carry explicit contract/design separation, a Spine context assembler that scopes agent context to an ownership path, a vertical-slice growth protocol that enforces hardest-first ordering, and a drift gate that makes spec-code divergence a blocking merge condition. The design synthesises well-established software engineering principles (Parnas information hiding, C4, ADRs, Walking Skeleton, Reflexion Models, Fitness Functions) into a lean, code-coupled, machine-enforced whole -- without the overhead of heavy-weight frameworks such as RUP or MDA.

cs.SE↗
arXiv2026

Beyond Fault Localization: A Trajectory-Level Study of LLM Agents for Microservice Root Cause Analysis

Automated microservice root cause analysis (RCA) is usually evaluated by whether the final answer localizes the fault source. This is insufficient for on-call site reliability engineers (SREs), who need the evidence and reasoning behind the fault. Evaluating the process is hard because agent trajectories interleave free-form reasoning with heterogeneous tool calls and telemetry results, making it difficult to assess agents' behavior. To assess how well RCA agents ground diagnoses in evidence, we propose a trajectory-analysis methodology that characterizes evidence navigation and utilization, SRE-oriented diagnostic intent, and recurring failure modes. Our key insight is that fault-propagation paths give a causal reference for where agents investigate and how they use evidence. With it, we analyze 5,000 trajectories from ten framework-backbone configurations (nine agent frameworks, two backbone models) on RCABench. Correct localization can hide deficiencies in causal reconstruction. Successful investigations use retrieved evidence to guide later queries, consider broader diagnostic intents, and investigate more deeply. Failed investigations drift toward unrelated services or repeat shallow queries; recurring failures arise from omitted evidence, misinterpreted evidence, and unsupported reasoning. To test whether these patterns can guide improvement, we build a lightweight defense prototype targeting the three failure families. Across two held-out benchmarks, two backbone models, and six diagnostic frameworks, the failure patterns recur; the prototype reduces their prevalence and raises Acc@1 by 3.5-35.1 percentage points in every configuration, suggesting that the mined failures generalize and are actionable. Our findings motivate a process-level view of automated RCA, in which trajectories reveal whether a diagnosis is correct, well-grounded, and open to improvement.

cs.SE↗