SWE bench - Moses Yebei

The Problem

We all know the struggle of sifting through GitHub issues, especially when they involve real-world software bugs. Developers waste too much time figuring out how to fix issues that could be resolved faster if they had a little help from AI. Enter SWE-bench, a benchmark designed to see if language models can actually generate useful patches for real issues. Spoiler: they can, but not always.

What This Does

SWE-bench is built to evaluate large language models in their ability to resolve GitHub issues. It uses a straightforward approach: given a codebase and an issue, a model generates a patch. You can find the core logic in the swebench/harness/grading.py file, which handles the evaluation process. The project is neatly organized, with a clear separation of concerns—swebench/collect for building datasets, swebench/inference for running the models, and docs/ for the documentation that helps you set everything up.

To get started, you’ll need Docker for a reproducible environment. The docker_setup.md in the docs/guides/ directory walks you through this. After setting it up, you can clone the repo and run a simple command to load the dataset:

Real-World Use

Imagine you’re knee-deep in a legacy project, and you stumble upon a cryptic issue that’s been sitting unresolved for weeks. Instead of pulling your hair out, you run SWE-bench. You load your codebase and the issue with the following snippet:

The model generates a patch, and you can quickly assess its viability. If it works, great! If not, at least you didn’t waste days trying to fix it manually.

The Bottom Line

SWE-bench is a solid tool for developers looking to integrate AI into their workflow. It’s not perfect—sometimes the generated patches are off, or it misses context—but it can save you time. If you work with large codebases and GitHub issues, give it a shot. Just don’t expect it to replace a good ol’ human developer anytime soon.

What the analyser found

Deployment readiness

3/7

Container image
CI pipeline
Lockfile committed
Test suite
README
License
No committed secrets

Composition

200 files

Python74
YAML66
Markdown26
Shell4
CSS2
TOML1

Docker

Module dependencies

The 10 most depended-upon modules of 89, from static import analysis. Red outline marks a module in an import cycle.

graph LR n0_h_harness_constants___init__["__init__"] n1__harness_test_spec_test_spec["test_spec"] n2_swebench_harness_utils["utils"] n3_webench_harness_docker_utils["docker_utils"] n4_nference_make_datasets_utils["utils"] n5_webench_harness_docker_build["docker_build"] n6_swebench_harness_grading["grading"] n7_swebench_collect_utils["utils"] n8_harness_log_parsers___init__["__init__"] n9_bench_harness_run_evaluation["run_evaluation"] n5_webench_harness_docker_build --> n0_h_harness_constants___init__ n5_webench_harness_docker_build --> n3_webench_harness_docker_utils n5_webench_harness_docker_build --> n1__harness_test_spec_test_spec n5_webench_harness_docker_build --> n2_swebench_harness_utils n6_swebench_harness_grading --> n0_h_harness_constants___init__ n6_swebench_harness_grading --> n1__harness_test_spec_test_spec n6_swebench_harness_grading --> n8_harness_log_parsers___init__ n9_bench_harness_run_evaluation --> n0_h_harness_constants___init__ n9_bench_harness_run_evaluation --> n3_webench_harness_docker_utils n9_bench_harness_run_evaluation --> n5_webench_harness_docker_build n9_bench_harness_run_evaluation --> n6_swebench_harness_grading n9_bench_harness_run_evaluation --> n1__harness_test_spec_test_spec n9_bench_harness_run_evaluation --> n2_swebench_harness_utils n1__harness_test_spec_test_spec --> n0_h_harness_constants___init__ n2_swebench_harness_utils --> n0_h_harness_constants___init__