README.md

metadata

license: cc-by-nc-4.0
pretty_name: MM-IssueLoc Bench
language:
  - en
task_categories:
  - text-retrieval
  - image-text-to-text
  - feature-extraction
tags:
  - multimodal
  - code-localization
  - repository-level
  - software-engineering
  - github-issues
  - benchmark
size_categories:
  - n<1K
configs:
  - config_name: canonical
    default: true
    data_files:
      - split: test
        path: data/canonical.parquet
  - config_name: function_level
    data_files:
      - split: test
        path: data/function_level.parquet

MM-IssueLoc Bench

MM-IssueLoc: A Controlled Benchmark for Evaluating Visual Evidence in Multimodal Repository-Level Issue Localization

📄 arXiv · 💻 Evaluation toolkit

Repository-level, multimodal issue → source-code localization. Given a GitHub issue (title + body + screenshots) and a target repository, retrieve the file(s) and function(s) that must be edited to resolve it — with visual evidence treated as an explicit input, decoupled from patch synthesis.

Config	Task	Output	Instances
canonical	File-level localization	Ranked files to edit	652
function_level	Function-level localization	Ranked file:function ids	343

Both configs ship a single test split (pure evaluation benchmark).

At a glance

• 652 instances (450 human-annotated + 202 AI-augmented), 1050 screenshots embedded as bytes.
• 23 languages — TypeScript 151, Python 126, JavaScript 120, C++ 45, Java 44, Go 33, C# 33, Rust 27, C 21, PHP 20, rest < 15.
• 7 image categories — ui_screenshot 177, behavior_demo 99, error_message 92, rendering_result 85, code_screenshot 84, log_output 66, data_visualization 50.
• 3 difficulty buckets (by changed_files) — easy 214 (1), medium 263 (2–3), hard 176 (≥4).
• Two-granularity gold — edit_files (all 652) and edit_functions (343, in function_level).

Usage

from datasets import load_dataset

ds = load_dataset("Jasaxion/MM-IssueLocBench", name="canonical", split="test")
row = ds[0]
row["images"][0]        # PIL.Image — first issue screenshot
row["edit_files"]       # list[str] — gold files to edit

fn = load_dataset("Jasaxion/MM-IssueLocBench", name="function_level", split="test")
fn[0]["edit_functions"] # list[str] — gold `path/to/file.py:Class.method` ids

function_level is the subset of canonical where supports_function_level is true and edit_functions is non-empty. For scoring, the evaluation toolkit provides the loader, metrics (Acc@K, MRR, Recall@K, Hit@K, MAP@K, NDCG@K), and CLIs.

Schema

Field	Type	Description
instance_id	string	Unique key <owner>__<repo>__<issue_id>.
annotation_by	string	"human" or "ai".
repo_full_name	string	GitHub owner/repo.
repo_language / language	string	GitHub primary language / language of edited files.
language_category	string	frontend / backend / systems / data_science / …
base_commit	string	PR base commit SHA — check out the repo here before evaluation.
issue_title / issue_body	string	Issue text (body in original markdown).
images	Sequence[Image]	Screenshots, decoded as PIL.Image.
image_paths / image_alts / image_sources	Sequence[string]	Per-image filename, alt text, provenance (body / comment / …), aligned with images.
image_category	string	One of the 7 categories.
relevance_score	int32	Annotator image–issue relevance score.
difficulty	string	easy / medium / hard, bucketed by changed_files.
diff	string	Full unified diff of the resolving PR (offline analysis only).
diff_files / diff_status	Sequence[string] / string	Files touched by diff / extraction status.
edit_files	Sequence[string]	Gold for file-level evaluation.
edit_functions	Sequence[string]	Gold for function-level evaluation (may be empty in canonical).
added_functions	Sequence[string]	Functions introduced by the patch — exclude at function-level eval time.
supports_function_level	bool	Whether function-level evaluation applies.
additions / deletions / changed_files / patch_count	int32	Diff size statistics.
repo_stars / repo_license	int32 / string	Repo stars at collection / SPDX license.

Repository snapshots

Evaluation runs against each repo at its base_commit; tarballs are not shipped (several GB, heterogeneous licenses). commit_cache.json maps instance_id → {repo, sha, dir_name}, and scripts/download_repos.py fetches them from the GitHub tarball API (a public_repo-scoped GITHUB_TOKEN is strongly recommended to avoid the 60 req/hour limit):

export GITHUB_TOKEN=ghp_xxx
python3 scripts/download_repos.py --workers 8   # --retry-failed to resume

Snapshots land under repos/<owner>__<repo>__<sha12>/; failures are logged to download_failures.json. See examples/preview/ for a zero-install skim of the data and examples/load_dataset.py for the full workflow.

Intended use & limitations

For benchmarking repository-level issue localization. Out of scope: patch generation, end-to-end training (652 instances is an evaluation set), and commercial use (CC BY-NC 4.0). Content skews toward web-ecosystem repos (TS/Py/JS ≈ 60%); per-row code licenses vary (repo_license). All data is from public GitHub issues and contains no PII beyond already-public author handles.

Citation

@article{zhan2026mm,
  title={MM-IssueLoc: A Controlled Benchmark for Evaluating Visual Evidence in Multimodal Repository-Level Issue Localization},
  author={Zhan, Shaoxiong and Hu, Shi and Feng, Boyu and Lin, Hai and Gong, Andrew and Zhou, Zhengda and Zhou, Jiaying and Hou, Yunyun and Su, Hao and Zheng, Hai-Tao},
  journal={arXiv preprint arXiv:2607.15205},
  year={2026}
}