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README.md
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path: data/train-*
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---
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language:
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- "en"
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pretty_name: "SWE-MERA"
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tags:
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- SWE
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- SWE-MERA
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- text-to-text
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- other
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- agent
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license: "mit"
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task_categories:
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- other
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---
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# SWE-MERA
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Continuously updated [SWE-MERA](https://mera-evaluation.github.io/demo-swe-mera/leaderboard) dataset
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### SWE-MERA splits:
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- **dev**: for testing (10 samples)
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- **lite**: presented at the leaderboard [here](https://mera-evaluation.github.io/demo-swe-mera/leaderboard) (750 samples)
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- **full**: continuously updated to collect more data (2738 samples)
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### Load dataset
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```python
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from datasets import load_dataset
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ds = load_dataset("MERA-evaluation/SWE-MERA", split='dev')
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```
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### Evaluation
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#### Description
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The main tool to validate tasks is **repotest** (available at [PyPI](https://pypi.org/project/repositorytest/) or [GitHub](https://github.com/MERA-Evaluation/repotest))
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`data.jsonl` - dataset file, where after the agent run, the `patch` column was changed
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#### Install dependencies
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```bash
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pip install repositorytest
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```
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#### Run evaluation
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```bash
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swemera --fn_input=data.jsonl --fn_output=submission.jsonl --column_patch=patch --mode=docker
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```
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If you don't want to use Docker and prefer Conda or local execution:
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```bash
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swemera --fn_input=data.jsonl --fn_output=submission.jsonl --column_patch=patch --mode=local
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```
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### Schema
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| Column Name | Type | Description | Is New Column |
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|--------------------------|----------------|--------------------------------------------------------------------------------------------|--------------|
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| repo | VARCHAR | Repository name (e.g., GitHub repository) | False |
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| instance_id | VARCHAR | Unique identifier for task: f"{repo.replace('/', '__') + '_' + pr_number}" | False |
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| base_commit | VARCHAR | Commit hash of the base code before patch applied | False |
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| patch | VARCHAR | Code patch or diff applied to the base_commit to be the correct solution | False |
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| test_patch | VARCHAR | Patch for test code, if separate from main code patch | False |
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| problem_statement | VARCHAR | Description or statement of the problem to solve | False |
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| hint_text | VARCHAR | Hints or tips for solving the problem from GitHub issue comments | False |
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| created_at | VARCHAR | Date when the instance was created | False |
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| closed_at | VARCHAR | Date when the instance was closed or resolved | True |
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| version | VARCHAR | Version identifier for the instance or dataset | False |
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| FAIL_TO_PASS | LIST[VARCHAR] | Test cases which were failing but later passed | False |
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| PASS_TO_PASS | LIST[VARCHAR] | Test cases which passed at all times | False |
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| environment_setup_commit | VARCHAR | Commit hash for environment setup configuration | False |
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| command_build | VARCHAR | Command used to build the repo | True |
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| command_test | VARCHAR | Command used to run the tests inside repo | True |
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| image_name | VARCHAR | Docker or container image name used for testing environment | True |
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| command_test_small | VARCHAR | Command to run a smaller subset of tests | True |
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| timeout_build | INTEGER | Timeout limit in seconds for build command | True |
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| timeout_test | INTEGER | Timeout limit in seconds for test command | True |
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| meta | DICT[STR,ANY] | Additional metadata about the instance* |
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*meta field description:
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```json
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{
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"score": {
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"task_correctness": "INTEGER",
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"test_correctness": "INTEGER",
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"complexity": "INTEGER",
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"test_completeness": "INTEGER"
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},
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"type": "VARCHAR",
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"pr_number": "INTEGER",
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"issue_number": "INTEGER",
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"merged_at": "VARCHAR",
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"tag": "VARCHAR // Information on how data was collected",
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"merge_commit": "VARCHAR",
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"task_id": "VARCHAR // Latency identifier of the task",
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"url": {
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"issue": "VARCHAR",
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"pr": "VARCHAR",
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"diff": "VARCHAR"
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},
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"command_test_small": "VARCHAR // Command to run only a small number of tests"
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}
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```
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### Dev tasks Examples
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### Instances
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| Instance ID | Short Title |
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|------------------------ |---------------------------------------------------------------------------------------------------------------------------------------|
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| reframe-0 | Performance threshold goes to -inf when it should be zero. |
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| pyflakes-1 | Walrus operator + annotation can cause F821 |
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| sqlglot-2 | MySQL dialect fails to parse PRIMARY KEY USING BTREE syntax |
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| matchms-3 | matchms fails when reading spectra where abundance is in scientific notation #809 |
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| guarddog-4 | Add Mach-O magic bytes to bundled binary detector #523 |
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| pdoc-5 | Include HTML headers in ToC |
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| QCElemental-6 | QCElemental allows floating point numbers for molecular charges, however when computing the spin with the fractional electron there is no way to represent the molecular multiplicity with just an integer. |
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| sqlglot-7 | TSQL: PRIMARY KEY constraint fails to parse if |
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| sqlglot-8 | [BUG] Conversion from SQL Server's DATEADD(quarter...) to PostgreSQL not working properly |
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| python-markdownify-9 | Inline Quotation element ignored |
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data/{train-00000-of-00001.parquet → dev-00000-of-00001.parquet}
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size
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version https://git-lfs.github.com/spec/v1
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oid sha256:6ef06a0c85653288492b6b70be0de008925fd3460b18096f30d7fa8ee8b886dd
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size 71903
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data/full-00000-of-00001.parquet
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version https://git-lfs.github.com/spec/v1
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oid sha256:89a99f66790f776230a05936573954fd19ca441071b1ed068d39f1310320957e
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size 265136797
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data/lite-00000-of-00001.parquet
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version https://git-lfs.github.com/spec/v1
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oid sha256:d7689ddad751954a12feab26f5e5a617cc05665ed8d0406edd533138cfed98f1
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size 12719810
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