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arxiv: 2605.15846 · v2 · pith:SPIPKLBHnew · submitted 2026-05-15 · 💻 cs.SE · cs.AI

RoadmapBench: Evaluating Long-Horizon Agentic Software Development Across Version Upgrades

Xinbo Xu , Ruihan Yang , Haiyang Shen , Wendong Xu , Bofei Gao , Ruoyu Wu , Kean Shi , Weichu Xie
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Xuanzhong Chen Ming Wu Jason Zeng Michael Heinrich Elvis Zhang Liang Chen Kuan Li Baobao Chang
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Pith reviewed 2026-05-20 16:53 UTC · model grok-4.3

classification 💻 cs.SE cs.AI
keywords RoadmapBenchcoding agentslong-horizon taskssoftware version upgradesAI evaluationmulti-file changesagentic development
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The pith

Current AI coding agents complete at most 39 percent of long-horizon version upgrade tasks drawn from real projects.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents RoadmapBench to measure how well AI agents handle the kind of extended software work that occurs during actual version upgrades. Each task starts from a real source snapshot and supplies a roadmap directing the agent to add the features present in the later target version, typically requiring changes to dozens of files and thousands of lines of code. Thirteen frontier models were tested across 115 such tasks spanning seventeen repositories and five languages. Even the strongest model succeeded on only 39.1 percent of the tasks while the weakest reached just 5.2 percent, a sharp drop from results on simpler single-bug benchmarks. The results indicate that sustained, multi-target development work remains beyond current agent capabilities.

Core claim

RoadmapBench consists of 115 tasks grounded in real open-source version upgrades; each supplies a source-version code snapshot plus a multi-target roadmap instruction, and requires the agent to produce the functionality present in the target version, with a median of 3,700 lines modified across 51 files. Systematic evaluation of thirteen frontier models shows top success at 39.1 percent and bottom success at 5.2 percent, in clear contrast to performance on existing bug-fix benchmarks.

What carries the argument

RoadmapBench benchmark, a set of 115 tasks each anchored in a real source-version snapshot paired with a multi-target roadmap that specifies the changes introduced in the target version.

If this is right

  • Existing single-issue bug-fix benchmarks substantially overestimate agent readiness for realistic engineering work.
  • Agents must improve coordinated planning across many files and multiple languages to approach professional-level upgrades.
  • Success rates vary widely across models, pointing to specific gaps in long-term reasoning rather than uniform limitations.
  • Version-upgrade tasks expose the need for evaluation metrics that track partial progress over extended sequences of edits.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Training pipelines may need to incorporate full project histories rather than isolated patches to close the observed gap.
  • Near-term deployments will likely require human oversight loops for any substantial version migration work.
  • Similar roadmap-style benchmarks could be constructed for domains such as database migrations or infrastructure changes to test generality.
  • If roadmap clarity proves insufficient, the benchmark results may partly reflect instruction ambiguity rather than pure model capability.

Load-bearing premise

The multi-target roadmap instructions supplied with each source snapshot fully specify every code change required for the target version without needing extra unstated context or human clarification.

What would settle it

A future model that reaches success rates above 70 percent on the identical RoadmapBench tasks under the same evaluation rules would directly challenge the claim that long-horizon development remains unsolved.

Figures

Figures reproduced from arXiv: 2605.15846 by Baobao Chang, Bofei Gao, Elvis Zhang, Haiyang Shen, Jason Zeng, Kean Shi, Kuan Li, Liang Chen, Michael Heinrich, Ming Wu, Ruihan Yang, Ruoyu Wu, Weichu Xie, Wendong Xu, Xinbo Xu, Xuanzhong Chen.

Figure 1
Figure 1. Figure 1: RoadmapBench Leaderboard. Resolved rate of top-performing models evaluated with Open￾Hands across 115 multi-target software evolution tasks spanning 5 languages and 17 repositories. Even the best-performing model resolves only 39.1% of tasks. †Corresponding authors: liangchen@unipat.ai, kuanli@unipat.ai, chbb@pku.edu.cn 1 arXiv:2605.15846v2 [cs.SE] 19 May 2026 [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of a ROADMAPBENCH task. The agent receives a source-version repository snapshot and a roadmap-style instruction, then implements the specified functionality inside a pinned Docker environment. Evaluation is performed via weighted subtask-level tests against behaviors introduced in the target version. requirements. As in real version upgrades, where multiple substantial changes are coordinated with… view at source ↗
Figure 3
Figure 3. Figure 3: Dataset overview of ROADMAPBENCH. (a) Task count per repository (outer ring) grouped by domain (inner ring): ML & Data (36), Web & RPC (17), ORM & Val (25), Infra & Tool (23), UI & Ren (14). (b) Distribution of ground-truth patch size (lines changed) per repository, where the dashed line marks the overall median of 3,714 LOC. diffs with release narratives to identify externally visible behavioral changes a… view at source ↗
Figure 4
Figure 4. Figure 4: ROADMAPBENCH construction pipeline. Repository mining selects task-ready version pairs; task construction aligns release narratives with code diffs to create instructions and tests. Static validation and rollout-based quality control repair task-side defects before benchmark inclusion. primary scaffold for all thirteen models. Each rollout runs inside a pinned Docker environment rooted at the source versio… view at source ↗
Figure 5
Figure 5. Figure 5: Efficiency and step-budget analysis. (a) Efficiency landscape of resolved rate versus average agent steps. Dashed lines mark fleet means, and shaded ellipses indicate performance tiers. (b) Cumu￾lative resolved rate under increasing per-task step budgets, showing how models convert additional compute into task resolution. substantial gap between the strongest models and the rest. Completion Score highlight… view at source ↗
Figure 6
Figure 6. Figure 6: Tool usage analysis. (a) Tool composition by model, decomposed into six intent categories and sorted by resolved rate. (b) Distribution of per-task tool call counts for three representative models spanning the full performance range: Seed-2.0-Pro (5%), Claude-Opus-4.7 (39%), and GPT-5.4 (30%). Vertical lines indicate mean values. region, indicating lower step efficiency. This contrast is particularly clear… view at source ↗
Figure 7
Figure 7. Figure 7: Resolved rate vs. three task complexity proxies (binned rate ± 95% Wilson CI). (a) Files changed, (b) lines changed, and (c) number of targets are all strong predictors of task difficulty, with monotonically decreasing resolved rates as complexity increases. uses only 36 tool calls on average and obtains a low resolved rate, suggesting insufficient repository interaction. GPT-5.4 uses 163 tool calls on ave… view at source ↗
Figure 8
Figure 8. Figure 8: Subtask pass rate for six representative models. (a) By change type. (b) By difficulty level. commands into a single structured JSON response per turn, a format these two models handle more effectively than the one-action-per-turn interface of OpenHands. 5.5 Target-Level Analysis We classify subtasks into five change types: Component Creation, Feature Addition, Feature Enhancement, Behavior Change, and Bug… view at source ↗
Figure 9
Figure 9. Figure 9: Error distribution for three representative models. Inner ring: category proportions; outer ring: sub-type breakdown. The dominant failure mode shifts from Implementation Error (strong models) to Build Error (weak models). and 33%, respectively. Seed-2.0-Pro is dominated by earlier construction failures, with Build Error and Missing Implementation accounting for 41% and 31% of failures. This pattern indica… view at source ↗
Figure 10
Figure 10. Figure 10: Task complexity overview and per-language breakdown (log-log scale). (a) All 115 tasks colored by language, with dashed lines at the benchmark medians (3,714 lines, 51 files). (b) Per-language panels: each language highlighted against the full benchmark (gray). thread-pool Fiber Glaze Slint Prisma Polars Falcon Ratatui Valibot Optuna MikroORM spaCy PyG Kitex Diesel Ruff Fyne 10 0 10 1 10 2 10 3 Files Chan… view at source ↗
Figure 11
Figure 11. Figure 11: Distribution of files changed (oracle patch) across repositories. Repos are sorted by median files changed (log scale); individual task values are shown as jittered points. 17 [PITH_FULL_IMAGE:figures/full_fig_p017_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Version upgrade trajectories. Each segment represents one task: hollow circles mark the source version, filled dots mark the target version. Solid lines indicate codebase growth; dashed lines indicate net size reduction. The temporal spread (2017–2026) and size diversity (20 KB–10 MB) demonstrate broad benchmark coverage. 18 [PITH_FULL_IMAGE:figures/full_fig_p018_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Examples of high-quality release documentation from three selected repositories. Each row shows cropped excerpts from a single version release, illustrating feature narratives, code examples, migration guides, and breaking-change descriptions that serve as source material for task construction. A task fails the compliance review if any item is marked FAIL. The synthesis agent revises the instruction or te… view at source ↗
Figure 14
Figure 14. Figure 14: Overall error distribution across all models (n=3,603 failed subtasks). Implementation Error dominates (39%), with Code Defect as the single largest sub-type. DeepSeek-V4-Pro (pass 51%). Profile resembles Opus but with more Build Errors (23% vs. 15%). Implementation Error remains dominant (51%), indicating strong architectural planning but less precise execution. Agent Failure is minimal (3%). GLM-5.1 (pa… view at source ↗
Figure 15
Figure 15. Figure 15: Error distribution for all thirteen analyzed models (inner ring: category proportions; outer ring: sub-type breakdown). Models are ordered by decreasing subtask pass rate from (a) to (m). The dominant failure mode shifts from Implementation Error (strong models) to Build Error and Missing Implementation (weak models). MiniMax-M2.7 (pass 36%). Highest Implementation Error percentage among mid-tier models (… view at source ↗
read the original abstract

Coding agents are increasingly deployed in real software development, where a single version iteration requires months of coordinated work across many files. However, most existing benchmarks focus predominantly on single-issue bug fixes from Python repositories, with coarse pass/fail evaluation outcomes, and thus fail to capture long-horizon, multi-target development at real engineering scale. To address this gap, we present RoadmapBench, a benchmark of 115 long-horizon coding tasks grounded in real open-source version upgrades across 17 repositories and 5 programming languages. Each task places the agent on a source-version code snapshot and provides a multi-target roadmap instruction requiring it to implement the functionality introduced in the target version, with a median modification of 3,700 lines across 51 files. We conduct a systematic evaluation on thirteen frontier models and find that even the strongest, Claude-Opus-4.7, resolves only 39.1% of tasks, while the weakest achieves merely 5.2%, in stark contrast to existing bug-fix benchmarks, suggesting that long-horizon software development remains a largely unsolved problem.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Circularity Check

0 steps flagged

No significant circularity in empirical benchmark results

full rationale

The paper constructs RoadmapBench from real open-source version upgrades across 17 repositories, supplies multi-target roadmap instructions with source snapshots, and reports success rates from direct execution of 13 external frontier models. The headline percentages (39.1% max, 5.2% min) are measured outcomes on these external tasks rather than quantities fitted to or defined by the benchmark itself. No equations, parameter fits, self-citation chains, or uniqueness theorems appear in the provided text that would reduce the central claim to a tautology or input by construction. The evaluation remains self-contained against external model runs and real repository diffs.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the assumption that the chosen version upgrades and supplied roadmaps form a representative and unambiguous set of long-horizon tasks; no free parameters or new invented entities are introduced.

axioms (1)
  • domain assumption Roadmap instructions derived from target versions accurately and completely specify the functionality that must be implemented.
    This premise defines task success and is invoked when the paper states that agents must implement the functionality introduced in the target version.

pith-pipeline@v0.9.0 · 5769 in / 1282 out tokens · 101781 ms · 2026-05-20T16:53:07.469218+00:00 · methodology

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Reference graph

Works this paper leans on

14 extracted references · 14 canonical work pages

  1. [1]

    Create public subpackage optuna/storages/journal/ containing: __init__.py, _base.py, _file.py, _redis.py,_storage.py

    work page

  2. [2]

    Class renames (importable from optuna.storages.journal): BaseJournalBackend (was BaseJournalLogStorage), BaseJournalSnapshot (was BaseJournalLogSnapshot), JournalFileBackend (was JournalFileStorage), JournalRedisBackend (was JournalRedisStorage), JournalFileSymlinkLock, JournalFileOpenLock,JournalStorage(unchanged)

    work page

  3. [3]

    BaseJournalLogStorage should subclassBaseJournalBackenddecorated with@deprecated_class

    Old names remain importable fromoptuna.storages with deprecation warnings. BaseJournalLogStorage should subclassBaseJournalBackenddecorated with@deprecated_class. 4.optuna.storages.journal.__all__ must include: JournalFileBackend, BaseJournalBackend, JournalFileOpenLock,JournalFileSymlinkLock,JournalRedisBackend,JournalStorage. 19 Target 3: Constrained Op...

    work page

  4. [4]

    constraints

    Helper module optuna/study/_constrained_optimization.py: define constant _CONSTRAINTS_KEY = "constraints". Implement _get_feasible_trials(trials) returning only trials where all constraint values are≤0.0. Trials without a"constraints"key are considered infeasible. 2.best_trial property: if the best trial is infeasible, filter to feasible trials and select...

    work page 2025

  5. [5]

    Specification clarity(Q1–Q3): each target’s goal and constraints are explicitly stated; the instruction is self-contained without referencing the construction process; requirements are defined positively rather than by exclusion

    work page

  6. [6]

    Implementation leakage(Q4): a systematic scan for five leakage types—algorithm/flow steps, internal naming, pseudo-code control flow, bug root-cause disclosure, and refactoring checklists—that reveal howto implement rather thanwhatbehavior is required

    work page

  7. [7]

    Information integrity(Q5–Q7): public API contracts are unambiguous; no test metadata (file names, function names, scoring details) is disclosed; no version numbers or repository names appear

    work page

  8. [8]

    Narrative quality(Q8–Q9): the instruction provides a coherent version narrative with clear prior- ity ordering among targets; individual target sections follow a consistent structure (background, requirements, constraints)

    work page

  9. [9]

    Test conventions(T1–T7): tests use the required directory layout; target weights sum to 1.0; tests are deterministic and environment-independent; tests do not check implementation internals beyond the specified public contract. 21 (a) Optuna v4.2(medium.com/optuna) (b) Kitex v0.12.0(cloudwego.io) (c) Ruff v0.12.0(astral.sh) Figure 13:Examples of high-qual...

    work page

  10. [10]

    Before”: initial validation; “After

    Minimality: tests performing only dead-letter matching without behavioral value are flagged for removal. Issues are classified asT-missing,T-ambiguous,T-incorrect, orT-other. Each confirmed issue is repaired by updating the instruction or test, and the oracle patch is re-run to confirm the fail-to-pass guarantee. G.3 Quality Control Protocol G.3.1 Attribu...

    work page

  11. [11]

    Reads the complete test output (test-stdout.txt) containing all subtask results

    work page

  12. [12]

    Reads the task specification (instruction.md) to understand requirements

    work page

  13. [13]

    Optionally inspects the agent’s final code or greps the trajectory for relevant context

    work page

  14. [14]

    analysis paralysis

    Outputs a structured classification for each failed subtask: category, sub-type, root-cause phrase (English, 2–5 words), and rationale (1–3 sentences with technical detail). The task-level approach (one classifier call per task, classifying all failed subtasks together) enables cross-subtask awareness—e.g., recognizing that multiple subtasks fail due to t...

    work page 2024