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arxiv: 2602.02262 · v3 · pith:LY736NPXnew · submitted 2026-02-02 · 💻 cs.SE · cs.AI· cs.CL

OmniCode: A Benchmark for Evaluating Software Engineering Agents

Atharv Sonwane , Eng-Shen Tu , Wei-Chung Lu , Claas Beger , Carter Larsen , Debjit Dhar , Simon Alford , Rachel Chen
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Ronit Pattanayak Tuan Anh Dang Guohao Chen Gloria Geng Kevin Ellis Saikat Dutta
This is my paper

Pith reviewed 2026-05-21 13:37 UTC · model grok-4.3

classification 💻 cs.SE cs.AIcs.CL
keywords software engineering benchmarkLLM coding agentsbug fixingtest generationcode reviewstyle fixingPython Java C++agent evaluation
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The pith

OmniCode benchmark shows agents achieve at most 25% success on C++ test generation.

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

The paper introduces OmniCode to fill a gap left by narrow benchmarks like HumanEval and SWE-Bench, which focus mainly on competition-style coding or patch fixes. It assembles 1794 tasks across Python, Java, and C++ in four categories: bug fixing, test generation, code review fixing, and style fixing. Tasks receive manual validation to remove ill-defined cases and are built synthetically or from recent sources to limit data leakage. Evaluations with frameworks such as SWE-Agent reveal strong results on some Python bug fixes but sharp drops elsewhere, including a peak of 25% with DeepSeek-V3.1 on C++ test generation. Readers would care because the work supplies a wider test of whether agents can handle the mix of activities real software teams perform daily.

Core claim

OmniCode contains 1794 tasks spanning three programming languages and four key categories, and popular agent frameworks such as SWE-Agent fall short on tasks such as Test Generation and in languages such as C++ and Java, with a maximum of 25.0% achieved with DeepSeek-V3.1 on C++ Test Generation.

What carries the argument

OmniCode benchmark, built through synthetic crafting from limited real-world examples combined with manual validation and recent curation to create leakage-resistant tasks across multiple software engineering categories.

If this is right

  • Agents require targeted improvements on test generation and on non-Python languages to reach usable performance.
  • Language-specific differences indicate that multilingual training or adaptation remains necessary.
  • Future agent development should target the full set of categories rather than isolated bug-fixing skills.
  • Synthetic generation from small seed sets can scale benchmark creation while preserving realism and freshness.

Where Pith is reading between the lines

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

  • Widespread use of OmniCode could redirect research effort toward agents that manage review and style tasks as well as core fixes.
  • The synthetic-crafting approach might transfer to other domains where public data is scarce or already overused in training.
  • If agents reach high scores here, it would support claims that they are approaching production-level software engineering capability.

Load-bearing premise

The tasks, after manual validation and synthetic creation, form a reliable proxy for the full range of real-world software engineering problems without hidden biases or excessive simplicity.

What would settle it

A new agent that consistently exceeds 60% success across all four categories and all three languages on the released OmniCode tasks would show the reported performance gaps are not inherent to current methods.

Figures

Figures reproduced from arXiv: 2602.02262 by Atharv Sonwane, Carter Larsen, Claas Beger, Debjit Dhar, Eng-Shen Tu, Gloria Geng, Guohao Chen, Kevin Ellis, Rachel Chen, Ronit Pattanayak, Saikat Dutta, Simon Alford, Tuan Anh Dang, Wei-Chung Lu.

Figure 1
Figure 1. Figure 1: OmniCode synthetically builds multiple tasks out of a base dataset to holistically evaluate software [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: In the Test Generation task, we evaluate proposed test patch against both the ground truth (gold) [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: In the task of responding to Code Review, an initial incorrect patch is pro￾vided, which contains a meaningful attempt at solving a given problem. This attempt is then reviewed by a human or an LLM, and a review report is generated. Utilizing this report, the LLM is tasked with correcting the initial approach, which is then validated with the normal testing suite. It is not uncommon for developers to itera… view at source ↗
Figure 4
Figure 4. Figure 4: In the Style Fix task, we first create task instances by running a style check tool on the whole [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Distribution of patch complexity scores for resolved versus unresolved instances. [PITH_FULL_IMAGE:figures/full_fig_p010_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Comparing agent performance at test generation if evaluating with only the gold patch versus using [PITH_FULL_IMAGE:figures/full_fig_p011_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Categorization of bad patches [PITH_FULL_IMAGE:figures/full_fig_p015_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Categorization of bad patches [PITH_FULL_IMAGE:figures/full_fig_p015_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Categorization of reviews To understand the distribution of bad patches generated by our pipeline, we categorize a sample of 100 Python bad patches with results displayed in [PITH_FULL_IMAGE:figures/full_fig_p015_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Distribution of agent failure modes for patches generated by SWE-Agent + Gemini-2.5-Flash on [PITH_FULL_IMAGE:figures/full_fig_p017_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Failure Mode Taxonomy of Agent-Generated Patches. [PITH_FULL_IMAGE:figures/full_fig_p018_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Resolve rates for the 20 most frequent style errors in Python. [PITH_FULL_IMAGE:figures/full_fig_p026_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Resolve rates for the 20 most frequent style errors in Java. [PITH_FULL_IMAGE:figures/full_fig_p027_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Resolve rates for the 20 most frequent style errors in CPP. [PITH_FULL_IMAGE:figures/full_fig_p029_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Bugfixing performance plotted together with performance on other tasks separately for each [PITH_FULL_IMAGE:figures/full_fig_p030_15.png] view at source ↗
read the original abstract

LLM-powered coding agents are redefining how real-world software is developed. To drive the research towards better coding agents, we require challenging benchmarks that can rigorously evaluate the ability of such agents to perform various software engineering tasks. However, popular coding benchmarks such as HumanEval and SWE-Bench focus on narrowly scoped tasks such as competition programming and patch generation. In reality, software engineers have to handle a broader set of tasks for real-world software development. To address this gap, we propose OmniCode, a novel software engineering benchmark that contains a broader and more diverse set of task categories beyond code or patch generation. Overall, OmniCode contains 1794 tasks spanning three programming languages - Python, Java, and C++ - and four key categories: bug fixing, test generation, code review fixing, and style fixing. In contrast to prior software engineering benchmarks, the tasks in OmniCode are (1) manually validated to eliminate ill-defined problems, and (2) synthetically crafted or recently curated to avoid data leakage issues, presenting a new framework for synthetically generating diverse software tasks from limited real-world data. We evaluate OmniCode with popular agent frameworks such as SWE-Agent and show that while they may perform well on bug fixing for Python, they fall short on tasks such as Test Generation and in languages such as C++ and Java. For instance, SWE-Agent achieves a maximum of 25.0% with DeepSeek-V3.1 on C++ Test Generation. OmniCode aims to serve as a robust benchmark and spur the development of agents that can perform well across different aspects of software development. Code and data are available at https://github.com/seal-research/OmniCode.

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.

Referee Report

1 major / 1 minor

Summary. The paper introduces OmniCode, a benchmark containing 1794 tasks in Python, Java, and C++ for four software engineering categories: bug fixing, test generation, code review fixing, and style fixing. Tasks are manually validated to remove ill-defined problems and designed to avoid data leakage through synthetic crafting or recent curation. Evaluations with agent frameworks such as SWE-Agent reveal performance gaps, particularly on test generation tasks and in C++ and Java, with the highest reported score being 25.0% for DeepSeek-V3.1 on C++ Test Generation.

Significance. Should the validation procedures prove robust upon detailed reporting, OmniCode would offer a valuable expansion over narrower benchmarks like SWE-Bench by covering a wider range of real-world software engineering activities across multiple languages. This could help identify weaknesses in current agents and guide improvements in handling diverse tasks.

major comments (1)
  1. The central reliability claim depends on manual validation of all 1794 tasks to eliminate ill-defined problems, yet the manuscript provides no information on the number of validators involved, their software engineering expertise, the specific criteria used for task rejection, or any inter-annotator agreement statistics. This omission makes it difficult to assess the effectiveness of the filtering process or the potential for unquantified bias in the final task set.
minor comments (1)
  1. The reported maximum of 25.0% on C++ Test Generation could benefit from additional context on the total number of tasks in that subcategory and variance across runs if applicable.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive feedback and recommendation. We address the single major comment below.

read point-by-point responses

  1. Referee: The central reliability claim depends on manual validation of all 1794 tasks to eliminate ill-defined problems, yet the manuscript provides no information on the number of validators involved, their software engineering expertise, the specific criteria used for task rejection, or any inter-annotator agreement statistics. This omission makes it difficult to assess the effectiveness of the filtering process or the potential for unquantified bias in the final task set.

    Authors: We agree that the current manuscript does not report these details on the manual validation process, which limits the ability to evaluate its robustness. This was an oversight in our presentation. In the revised version we will add a dedicated subsection describing the validation procedure, including the number of validators, their software engineering backgrounds and expertise, the precise rejection criteria applied to remove ill-defined tasks, and inter-annotator agreement statistics. We have retained the original validation records and will incorporate this information transparently to strengthen the reliability claims. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical benchmark with independent task construction

full rationale

The paper constructs and evaluates a benchmark (OmniCode) through manual validation of 1794 tasks and empirical testing of external agent frameworks such as SWE-Agent. No derivation chain, equations, fitted parameters, or predictions are claimed. Central claims rest on the described curation process and observed performance numbers rather than reducing to self-citation or input-by-construction. This is a standard empirical benchmark paper whose methodology is externally verifiable via the released code and data.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

Based on abstract only; no explicit free parameters, axioms, or invented entities are stated. The central claim rests on the unelaborated premise that manual validation and recent curation produce representative, leakage-free tasks.

axioms (2)
  • domain assumption Manual validation by the authors eliminates ill-defined problems and ensures task quality.
    Stated in the abstract as a contrast to prior benchmarks.
  • domain assumption Synthetically crafted or recently curated tasks avoid data leakage from model training sets.
    Presented as a key property of OmniCode in the abstract.

pith-pipeline@v0.9.0 · 5889 in / 1343 out tokens · 33003 ms · 2026-05-21T13:37:45.887069+00:00 · methodology

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Forward citations

Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. ScarfBench: A Benchmark for Cross-Framework Application Migration in Enterprise Java

    cs.SE 2026-05 accept novelty 8.0

    ScarfBench supplies 204 cross-framework Java migration tasks where the best agent passes only 15.3% of focused and 12.2% of whole-application tests.

  2. ScarfBench: A Benchmark for Cross-Framework Application Migration in Enterprise Java

    cs.SE 2026-05 unverdicted novelty 7.0

    ScarfBench supplies 34 Java applications yielding 204 directed cross-framework refactoring tasks and shows state-of-the-art agents achieve only 15.3% test pass on focused migrations and 12.2% on whole applications.

  3. Reproduction Test Generation for Java SWE Issues

    cs.SE 2026-05 unverdicted novelty 7.0

    Presents the first benchmark and adapted solution for generating reproduction tests from Java software issues.

  4. Reproduction Test Generation for Java SWE Issues

    cs.SE 2026-05 unverdicted novelty 6.0

    Introduces the first benchmark for Java reproduction test generation from repository issues and adapts a prior Python tool to produce high performance on it.

  5. Multilingual Prompt Localization for Agent-as-a-Judge: Language and Backbone Sensitivity in Requirement-Level Evaluation

    cs.CL 2026-04 unverdicted novelty 6.0

    Localizing judge prompts to five languages shows that LLM backbones interact with language in agent-as-a-judge evaluations, inverting rankings and revealing no universal best model with low inter-judge agreement.

Reference graph

Works this paper leans on

23 extracted references · 23 canonical work pages · cited by 3 Pith papers · 1 internal anchor

  1. [1]

    SWE-smith: Scaling Data for Software Engineering Agents

    URLhttps://arxiv.org/abs/2504.21798. Daoguang Zan, Zhirong Huang, Ailun Yu, Shaoxin Lin, Yifan Shi, Wei Liu, Dong Chen, Zongshuai Qi, Hao Yu, Lei Yu, et al. Swe-bench-java: A github issue resolving benchmark for java.arXiv preprint arXiv:2408.14354, 2024. Daoguang Zan, Zhirong Huang, Wei Liu, Hanwu Chen, Linhao Zhang, Shulin Xin, Lu Chen, Qi Liu, Xiaojian...

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  2. [2]

    **Introduce one to two subtle, functional bugs ** without adding any comments

    work page

  3. [3]

    **Do NOT break compilation ** and **do not introduce any syntax or spelling errors ** or make any code-style changes

    work page

  4. [4]

    **Do NOT change any import statements **

    work page

  5. [5]

    Preserve formatting and comments; modify only the minimum lines needed to trigger a logical failure under certain inputs

    work page

  6. [6]

    Return **only** the full modified file content, with no explanations or diff markers. --- {path} original content START --- {curr_text} --- {path} original content END --- 20 SWE-Agent Bug-fixing instructions <uploaded_files> {{working_dir}} </uploaded_files> I’ve uploaded a python code repository in the directory {{ working_dir}}. Consider the following ...

    work page

  7. [11]

    SWE-Agent Test Generation instructions <uploaded_files> {{working_dir}} </uploaded_files> I’ve uploaded a python code repository in the directory {{ working_dir}}

    Think about edgecases and make sure your fix handles them as well Your thinking should be thorough and so it’s fine if it’s very long. SWE-Agent Test Generation instructions <uploaded_files> {{working_dir}} </uploaded_files> I’ve uploaded a python code repository in the directory {{ working_dir}}. Consider the following problem description: <problem_descr...

    work page

  8. [12]

    As a first step, it might be a good idea to find and read code relevant to the <problem_description>

    work page

  9. [13]

    Create a script to reproduce the error and execute it with ‘ python <filename.py>‘ using the bash tool, to confirm the error

    work page

  10. [14]

    Edit the the testing suite of the repo to implement a test based on this reproduction script which can be run using the repository’s testing infrastructure / tooling (e.g. pytest)

    work page

  11. [15]

    Ensure this test runs and successfully reproduces the problem !

    work page

  12. [16]

    Your thinking should be thorough and so it’s fine if it’s very long

    Remove the reproduction script and only keep changes to the test suite that reproduce the problem. Your thinking should be thorough and so it’s fine if it’s very long. SWE-Agent Style-Fix instructions You have recently generated a patch to resolve an issue within this repository. Pylint has been run on the modified files and has produced the following fee...

    work page

  13. [17]

    Analyze the Pylint violations provided in the problem statement

    work page

  14. [18]

    Understand the specific rules that were violated (e.g., naming conventions, unused imports, complexity issues)

    work page

  15. [19]

    Apply fixes that resolve these errors while maintaining code functionality

    work page

  16. [20]

    Ensure your changes follow Python best practices and improve code readability

    work page

  17. [21]

    Test that your fixes don’t introduce new Pylint violations

    work page

  18. [22]

    Focus on the most critical violations first and ensure your fixes improve overall code quality and maintainability

    Do not introduce any new files to fix the style errors Common Pylint violations you may encounter: - Naming and style issues (invalid-name, missing-docstring, line- too-long) 22 - Import issues (unused-import, wrong-import-order, reimported) - Error-prone patterns (undefined-variable, no-member, unsubscriptable-object) - Code design issues (too-many-argum...

    work page

  19. [23]

    As a first step, it might be a good idea to find and read code relevant to the <pr_description>

    work page

  20. [24]

    Create a script to reproduce the error and execute it to confirm the error

    work page

  21. [25]

    Edit the sourcecode of the repo to resolve the issue

    work page

  22. [26]

    Rerun your reproduce script and confirm that the error is fixed!

    work page

  23. [27]

    C.1 RULESETS USED FORSTYLEREVIEW See Table 7, Table 8, and Table 9 for the list of style errors we account for in our Style Review tasks

    Think about edgecases and make sure your fix handles them as well Your thinking should be thorough and so it’s fine if it’s very long. C.1 RULESETS USED FORSTYLEREVIEW See Table 7, Table 8, and Table 9 for the list of style errors we account for in our Style Review tasks. 23 Table 7: List of Python Style Errors. protected-access redefined-outer-name unuse...

    work page