---
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](https://arxiv.org/abs/2607.15205) · [💻 Evaluation toolkit](https://github.com/Jasaxion/MM-IssueLoc-Bench)

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

```python
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](https://github.com/Jasaxion/MM-IssueLoc-Bench) 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):

```bash
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

```bibtex
@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}
}
```