Datasets:
Formats:
json
Languages:
English
Size:
< 1K
Tags:
distributed-systems
debugging
root-cause-analysis
fault-localization
agents
software-engineering
License:
| 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. | |
| - Paper: *DistDebug-Bench: Can LLM Agents Diagnose Bugs in Distributed Systems?* (NeurIPS 2026, Track on Evaluations and Datasets) | |
| - Code, harness, and evaluator: https://github.com/OrderLab/distdebug-bench | |
| - Website and leaderboard: https://distdebug.com | |
| - Agent traces behind the paper's Table 1: https://huggingface.co/datasets/OrderLab/distdebug-bench-traces | |
| - Version: 1.0.0 | |
| ## 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](https://mlcommons.org/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 | |
| ```python | |
| 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](https://github.com/mlcommons/croissant), every record set, | |
| including the screenshots and logs, can be loaded from the metadata alone: | |
| ```python | |
| 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 | |
| ```bibtex | |
| @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} | |
| } | |
| ``` | |