AI code agents are increasingly deployed to resolve real software issues, yet their reliability under superficial code variations remains poorly understood. We evaluate whether coding agents that repair repository-level issues remain reliable when the surrounding codebase is rewritten into a semantically equivalent form. We introduce a random variant sampler that applies common semantics-preserving transformations (SPTs) - spanning control-flow rewrites, dead-code injection, and identifier renaming - to produce perturbed variants. We evaluate two agentic scaffolds (mini-SWE agent and OpenCode) each backed by one of four frontier models (Claude Opus 4.5, Kimi K2.5, MiniMax M2.5, and Qwen 3.6-27B) across instances drawn from SWE-bench Verified and SWE-bench Pro. For each instance, the agent is run multiple times on the unperturbed and perturbed variants, yielding paired resolve-rate estimates that isolate the perturbation effect from intrinsic stochasticity. We find small degradation in most configurations: up to 6.7 percentage points mean resolve-rate drop in the most affected configurations with statistically significant degradations in 6 of 16 configurations of model, scaffold, and dataset. Crucially, no single model ranking by robustness holds across scaffolds - Qwen is among the most robust under mini-SWE agent on SWE-bench Verified yet the most brittle under OpenCode - revealing a jagged robustness frontier. The simpler scaffold (mini-SWE agent) is more robust to perturbation. Our results demonstrate that even top frontier models are susceptible to semantics-preserving perturbations although the effect is not uniform, raising concerns about the deployment reliability of AI code agents in diverse real-world codebases.
Figures & tables
Transformation
Summary
If Else Switcher
Swaps if/else branches and negates the condition
For Loop Rewriting
Rewrites a for loop using an explicit iterator
And Condition Splitter
Decomposes if A and B into nested if s
Comparison Swapper
Swaps operands and inverts the operator
While Loop Unrolling
Unrolls one iteration of a while loop
Double Negation Injector
Wraps a condition in not not ( ⋅ )
Table 1: Catalog of semantics-preserving transformations (SPTs).
Figure 1: Mean degradation Δˉ (percentage points) for each model under each scaffold on both benchmarks. Error bars are fixed-population 95% bootstrap confidence intervals.
Figure 2: Mean relative change in agent steps ( δˉstep ) and cost ( δˉcost ) after perturbation, for each scaffold–model configuration on the 28 SWE-bench Verified instances (left) and the 26 SWE-bench Pro instances (right), restricted to runs the agent resolved in both cases. Positive values mean perturbed runs take more steps, or cost more, than unperturbed runs on the same instance. Error bars are fixed-population 95% bootstrap confidence intervals.
Category
mini-SWE
OpenCode
Architecture
Single, centralized agent
Primary agents invoke tools and subagents for specific tasks (e.g., Build, Plan, General, Explore)
Available tools determined by an agent’s role and user configuration
Context Management
Continuous, append-only
Multiple, dynamically changing contexts for different subagents
Table 2: Comparison of mini-SWE and OpenCode Agent Scaffolds
Figure 3: Mean resolve rate (%) on unperturbed and perturbed runs for each model under each scaffold, on SWE-bench Verified (top) and SWE-bench Pro (bottom).
Figure 4: Mean relative change in agent steps ( δˉstep ) and cost ( δˉcost ) after perturbation over all runs, for each scaffold–model configuration on the 28 SWE-bench Verified instances (left) and the 26 SWE-bench Pro instances (right). Positive values mean perturbed runs take more steps, or cost more, than unperturbed runs on the same instance. Error bars are fixed-population 95% bootstrap confidence intervals.
Figure 5: Per-instance degradation Δ(i) for the 28 SWE-bench Verified instances under each configuration. Error bars are Newcombe 95% confidence intervals for the difference between the unperturbed and perturbed resolve proportions ( n=20 runs per condition).
Figure 6: Per-instance degradation Δ(i) for the 26 SWE-bench Pro instances under each configuration. Error bars are Newcombe 95% confidence intervals for the difference between the unperturbed and perturbed resolve proportions ( n=20 runs per condition).
Code agents resolve 65-70% of SWE-bench Verified issues, but Pass@1 cannot tell us why the rest fail, and, as we show, capable-model failures are systematically misdiagnosed without trajectory data. We introduce TRAJEVAL, a training-free decomposition of agent trajectories into reference-patch-aligned search, read, and edit stages, and apply it across 16,758 trajectories spanning three architectures and seven models. The dominant failure of capable models is not localization: 60-69% of failures on SWE-Agent and OpenHands reach and edit the correct functions yet still produce incorrect patches, and the pattern persists for most models on the bash-only LiveSWEAgent. Within this Edit-Quality residual, we identify Coherence Collapse, where the agent reaches correct code and then overwrites or thrashes it, as the largest theme, replicating across SWE-bench Verified and the multilingual PolyBench Verified. In 5 cases, the agent produces a patch bit-identical to the gold reference mid-trajectory and destroys it later; an edit-commit checkpoint recovers all 5 against the SWE-bench Docker harness. A reference-free consensus-driven variant yields a directional +3.0 pp Pass@1 measurement on GPT-5 (p=0.08).
Autonomous coding agents solve repository issues by reading code, running commands, editing files, and submitting patches. Extra inference-time compute yields gains only when it produces a useful repair and supplies reliable evidence for choosing one. Three behaviors decide both, and we argue they are teachable rather than byproducts of scale, so a policy can carry them instead of a scaffold. Location diversity remains narrow, since attempts return to the same site and extra samples add no coverage. Edit diversity is left unexploited, since methodologies that differ resolve complementary issues no single run reaches. Verification misleads, since a test the agent writes for its own patch accepts many incorrect ones. Directing search by execution feedback and scoring each patch against its own reverted tree resolves 52.8% of SWE-bench Verified using 48.1% of the agent-steps an eight-sample baseline spends. Training moves these behaviors into the policy. On the 270 issues held out from SFT and RL training, weighted supervised fine-tuning raises pass@1 from 31.9% to 35.2% and pass@8 from 46.7% to 51.1%. A reinforcement objective then trains the verifier against gold-labeled repairs and incorrect variants, crediting the assertions that detect them. It raises pass@1 to 43.0% and pass@8 to 60.7%, lifts verifier precision from 26.8% to 41.7%, and more than halves false acceptance. Resolution improves on two of three out-of-distribution suites and verifier precision on all three, and the gains hold at 7B, 14B, and 30B against published coder baselines.
Muhammad Ahmed Mohsin, Myeongsoo Kim, Kangrui Ruan +3
Code agents are currently having skillful performance on repository-level software engineering benchmarks, but it remains unclear whether success on end-to-end tasks such as issue resolution truly reflects repository context reasoning, the ability to identify the task-relevant information across multiple files and reason over the relations among them. To investigate this question, we introduce RepoMirage, a two-stage evaluation suite built on SWE-Bench Verified that adopts perturbation as a diagnostic tool to increase the demand for context reasoning by transforming how the repository is exposed. First, RepoMirage-Perturb applies three types of semantics-preserving repository-level perturbations, revealing a clear performance drop when correct solving requires broader context access. RepoMirage-Extend further turns perturbation-targeted structural bottlenecks into explicit tasks beyond issue resolution, where the average performance declines from 66.8% in the original setting to 25.3%, indicating a significant deficiency in repository context reasoning. Further trajectory analysis reveals an exploration drift, where agents access broader repository context but fail to turn it into effective structure information. Motivated by this observation, we propose RepoAnchor, a structure-first prototype workflow that separates repository exploration from downstream problem solving, and show that explicit structural scaffolding yields notable gains. These results uncover an previously overlooked gap in repository context reasoning for code agents and suggest that stronger structure-aware methods are potential to improve them.
Hanyu Li, Yichi Zhang, Speed Zhu +3
Beijing University of Posts and Telecommunications · Tsinghua University · Tencent