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OrderLab
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distdebug-bench

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distributed-systems
debugging
root-cause-analysis
fault-localization
agents
software-engineering
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metadata 
license: mit
language:
  - en
pretty_name: DistDebug-Bench
size_categories:
  - n<1K
tags:
  - distributed-systems
  - debugging
  - root-cause-analysis
  - fault-localization
  - agents
  - software-engineering
configs:
  - config_name: instances
    data_files:
      - split: test
        path: data/instances.jsonl
    default: true
  - config_name: gold_blocks
    data_files:
      - split: test
        path: data/gold_blocks.jsonl
  - config_name: systems
    data_files:
      - split: test
        path: data/systems.jsonl

DistDebug-Bench

DistDebug-Bench evaluates whether LLM agents can find the root cause of real-world bugs in distributed systems. It contains 120 resolved bugs from 11 open-source systems. For each bug, an agent receives the original issue report, any attached logs and screenshots, and a checkout of the system's source at the pre-fix snapshot. It must name the responsible files, functions, and line ranges. Diagnoses are scored against expert-annotated root-cause blocks.

Contents

Config / path Rows Description
instances (data/instances.jsonl) 120 One row per bug: issue report, pre-fix snapshot, links, and metadata.
gold_blocks (data/gold_blocks.jsonl) 218 One row per gold root-cause block, keyed by instance_id.
systems (data/systems.jsonl) 11 Source systems with repository URLs and upstream licenses.
artifacts/<instance_id>/ 17 screenshots, 4 logs Files attached to the issue reports of 7 bugs.

croissant.json describes all of the above, including the attachments, in Croissant format with Responsible AI fields.

What the agent sees

The task input is title, description, the bug's attachments, and the system's repository checked out at branch. The remaining columns (issue_url, fix_url, and the characterization labels) are for analysis and must not be shown to the agent: the links lead to the fix. The harness in the GitHub repository prepares workspaces with anonymized paths and without commits after the snapshot.

Gold blocks

Each block gives a repository-relative file, the enclosing function, and a start_line/end_line range in the pre-fix snapshot, scored as half-open [start_line, end_line). Blocks capture the annotated causal implementation established through issue resolution, not every location where the bug could be repaired. Score diagnoses with the evaluator in the GitHub repository; it reproduces the paper's scores and reports file-, function-, line-, and block-level coverage and precision, plus strict block coverage and mention coverage.

DistDebug-Bench Lite

DistDebug-Bench Lite is a 34-bug subset (lite == true), selected for report and label quality and for lower evaluation cost. Report Lite results separately from full-benchmark results.

Loading 

from datasets import load_dataset

instances = load_dataset("OrderLab/distdebug-bench", "instances", split="test")
gold = load_dataset("OrderLab/distdebug-bench", "gold_blocks", split="test")
lite = instances.filter(lambda row: row["lite"])

With mlcroissant, every record set, including the screenshots and logs, can be loaded from the metadata alone:

import mlcroissant as mlc

ds = mlc.Dataset("https://huggingface.co/datasets/OrderLab/distdebug-bench/resolve/main/croissant.json")
for record in ds.records("artifact_logs"):
    print(record["artifact_logs/instance_id"], record["artifact_logs/file_name"])

Systems and licenses

System Language Issue tracker Upstream license Bugs Lite
Elasticsearch java GitHub Issues AGPL-3.0-only OR SSPL-1.0 OR Elastic-2.0 19 4
etcd go GitHub Issues Apache-2.0 8 1
HBase java Apache JIRA Apache-2.0 5 3
HDFS java Apache JIRA Apache-2.0 10 3
Ignite java Apache JIRA Apache-2.0 3 2
Ignite 3 java Apache JIRA Apache-2.0 7 2
Kafka java Apache JIRA Apache-2.0 8 8
Kubernetes go GitHub Issues Apache-2.0 10 1
Ray python GitHub Issues Apache-2.0 21 4
TiKV rust GitHub Issues Apache-2.0 11 3
ZooKeeper java Apache JIRA Apache-2.0 18 3

The annotations, metadata, and code in this release are under the MIT license. Issue titles, descriptions, and attachments are reproduced from public issue trackers and remain subject to the terms of those trackers and projects. The dataset references paths and line numbers in the upstream repositories but does not redistribute their source code; the licenses above are those of the repositories' current default branches, and historical snapshots may differ.

Limitations

The benchmark covers resolved bugs in public, open-source distributed systems and evaluates code-level diagnosis only. Selection favors bugs whose fixes support precise labeling. Because the issues are public, models may have seen them during pretraining; the paper reports leakage controls and contamination probes. See the paper and croissant.json for the full discussion.

Citation 

@inproceedings{jiang2026distdebugbench,
  title     = {{DistDebug-Bench}: Can {LLM} Agents Diagnose Bugs in Distributed Systems?},
  author    = {Jiang, Yuxuan and Liu, Chenglin and Rose, John and Miller, Mason and Thavamani, Ruthesh and Wu, Juntao and Huang, Yinwei and Xu, Jiahao and Huang, Peng},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS), Track on Evaluations and Datasets},
  year      = {2026}
}