| --- |
| license: mit |
| language: [en] |
| pretty_name: foresight-swe-mini |
| tags: [benchmark, coding-agents, software-engineering, subset, foresight-swe] |
| size_categories: [n<1K] |
| configs: |
| - config_name: default |
| data_files: |
| - split: test |
| path: data/tasks.jsonl |
| --- |
| |
| # foresight-swe-mini |
|
|
| 14 of the 212 tasks in [Foresight-SWE](https://huggingface.co/datasets/shaswat123/Foresight-SWE), chosen to match the full benchmark's mix of languages, difficulty and defect types while keeping runs short. |
|
|
| Foresight-SWE asks whether a coding agent avoids a bug nobody told it about. Each task pairs a real pull request that introduced a defect (PR-1) with the later one that fixed it (PR-2). The agent gets only PR-1's task. Resolve rate counts PR-1's tests passing. Foresight resolve rate (FRR) also requires PR-2's held-out tests to pass. See the [paper](https://arxiv.org/abs/2611.01181) and [code](https://github.com/shaswatpatel123/Foresight-SWE). |
|
|
| **Read this first.** Unlike the other mini benchmarks in this collection, this subset has not been checked against real per-task results, because upstream publishes only totals and per-model breakdowns. The accuracy figures below are simulated. |
|
|
| **Changes.** The subset started with 15 tasks. Running upstream's gold patches through the upstream harness passed PR-1's tests on 14. The 15th, `pandas__49324-49704`, cannot be graded: its PR-1 install recipe installs numpy 2 before building pandas, the build fails, and no PR-1 test runs even for the gold patch. It was removed, and every number on this card is for the remaining 14. The gold patches also pass PR-2's held-out tests on 1 of the 14, so a patch identical to upstream's PR-1 would score 1 of 14 on FRR. |
|
|
| ## What it can tell you |
|
|
| One task is worth 7.1 points, and at leaderboard rates most models would pass 1 to 4 of the 14. Use it to spot large differences, or to compare two setups on the same tasks. Do not use it to rank close models or to estimate a full-benchmark score. |
|
|
| Simulated against the 11 models on the leaderboard: |
|
|
| | tasks | typical FRR error (points) | pairs 5+ points apart in the right order | pairs 10+ points apart in the right order | |
| |---:|---:|---:|---:| |
| | **foresight-swe-mini (14)** | **6.9** | **73%** | **82%** | |
| | random 10 | 8.6 | 64% | 73% | |
| | random 14 | 7.0 | 72% | 82% | |
| | random 20 | 5.9 | 79% | 88% | |
| | random 30 | 4.7 | 85% | 94% | |
| | random 60 | 3.1 | 95% | 99% | |
| | random 100 | 2.0 | 98% | 100% | |
|
|
| The 14 do no better than a random 14 on these numbers. The published task labels explain only 4 to 13% of each model's pass or fail outcomes, so matching on them cannot do much for accuracy. What matching adds is a guaranteed mix and no very slow tasks. A subset chosen from real per-task results might do better; that needs data upstream has not released. |
|
|
| The simulation fits each model to Tables D.1 and D.2 of the paper, then adds a difficulty shared across models and noise per model. Those two additions are assumptions. Numbers are the middle of three runs with different shared-difficulty settings, which changed little. |
|
|
| ## The mix |
|
|
| | | foresight-swe-mini | exact share of 14 | |
| |---|---:|---:| |
| | Python / JavaScript / TypeScript / Go | 7 / 3 / 2 / 2 | 7.6 / 3.2 / 1.8 / 1.4 | |
| | PR-1 easy / medium / hard | 2 / 6 / 6 | 3.1 / 5.1 / 5.8 | |
| | PR-2 easy / medium / hard | 9 / 3 / 2 | 8.3 / 3.4 / 2.3 | |
| | Major / minor | 9 / 5 | 8.5 / 5.5 | |
| | Silent / loud | 9 / 5 | 8.5 / 5.5 | |
| | Bug / feature / mixed | 7 / 6 / 1 | 7.4 / 5.8 / 0.8 | |
| | Security | 1 | 1.2 | |
|
|
| The 14 cover 10 of the 35 repositories, with at most 3 tasks from any one. Predicted FRR on the subset is within 1.6 points of the published score for all 11 models (`data/leaderboard.csv`). That only checks the mix, not real results. |
|
|
| ## How the tasks were chosen |
|
|
| Simulated annealing over the 158 tasks with at most 2,000 regression tests, 100 task tests, 20 held-out tests, a 60 KB patch and 3.5 GB of images. It minimises the difference from the full benchmark in the mix above, in each model's predicted scores and in task size, with a cap per repository. The search picked 15; the broken task was then dropped. `python build.py verified.jsonl` reproduces the selection, the removal and every number here. Exact task IDs are in `data/tasks.jsonl`. |
|
|
| ## Running it |
|
|
| The tasks live upstream; this dataset only tells you which 14 to run. Build a task file: |
|
|
| ```python |
| from datasets import load_dataset |
| ids = set(load_dataset("LocalLLaMA/foresight-swe-mini", split="test")["instance_id"]) |
| tasks = load_dataset("shaswat123/Foresight-SWE", split="test") |
| tasks.filter(lambda r: r["instance_id"] in ids).to_json("foresight_mini.jsonl") |
| ``` |
|
|
| Then set `DATASET` to that file in one of the [upstream eval scripts](https://github.com/shaswatpatel123/Foresight-SWE/tree/main/eval). Each task needs one amd64 Docker image, about 10 GB in total. To compare with the leaderboard, keep the paper's budgets: 250 to 350 steps and $3 per task in mini-swe-agent. |
|
|
| Report resolve rate and FRR, and label results "foresight-swe-mini". Scores on the 14 are not Foresight-SWE scores. |
|
|
| ## Credit |
|
|
| All tasks and the benchmark design are by Patel, Liao, Henderson, He and Chen. |
|
|
| ```bibtex |
| @misc{patel2026codingagentscatchbugs, |
| title={Do Coding Agents Catch Their Own Bugs? (They Don't.)}, |
| author={Shaswat Patel and Michel Liao and Peter Henderson and He He and Valerie Chen}, |
| year={2026}, |
| eprint={2611.01181}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.SE}, |
| url={https://arxiv.org/abs/2611.01181}, |
| } |
| ``` |
|
|
