# SWE-bench/SWE-bench

SWE-bench: Can Language Models Resolve Real-world Github Issues?

Repository: https://github.com/SWE-bench/SWE-bench
Canonical: https://ross.abutalabs.com/products/swe-bench
Homepage: https://www.swebench.com
Language: Python
License: MIT
License Family: permissive
Topics: benchmark, language-model, software-engineering
Last push: 2026-08-18T23:53:40+00:00

## Health v2 (maintenance only)
Score: 72/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 98, release rhythm 35, longevity 76
- inputs: {"age_days": 1065, "days_push": 15, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 5719, forks 953 (observed 2026-08-28T04:09:28.240988+00:00)

## What it is
SWE-bench is a benchmark and evaluation harness that tests whether large language models can resolve real-world GitHub issues by generating patches against actual codebases. It ships datasets (SWE-bench, Lite, Verified, Multimodal), a Docker-based reproducible evaluation harness, and official leaderboards.

## Use cases
- evaluate llm on real github issues
- benchmark coding agents on software engineering tasks
- measure how well a model generates bug-fix patches
- compare coding assistants on a public leaderboard
- run reproducible docker-based code evaluations
- find a dataset of real software engineering problems for llm research

## When to choose
- you need a standardized, reproducible benchmark for LLM software-engineering ability
- you want to compare your model or agent against published state-of-the-art results
- you need real-world issue/patch data rather than synthetic coding tasks

## When to avoid
- you need a general coding benchmark for simple algorithmic problems (e.g., HumanEval-style)
- you cannot run Docker or containerized evaluations
- you want a lightweight unit-testing framework rather than an LLM evaluation suite

## Facets
- artifact type: dataset
- maturity: active
- function: benchmarking, testing, machine-learning, llm-inference, agent-framework
- domain: large-language-models, developer-tools, machine-learning, testing
- platform: python
- tags: llm-benchmark, code-generation, github-issues, evaluation-harness, leaderboard, swe-agent, patch-generation, software-engineering, docker, linux, macos

## Member repositories
- SWE-bench/SWE-bench (main) score 72

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:28.240988+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:53:28.226782+00:00, confidence not recorded.
  - readme: https://github.com/SWE-bench/SWE-bench (fetched 2026-08-28T04:09:28.240988+00:00, sha e964a7f0420c)
  - homepage: https://www.swebench.com (fetched 2026-08-29T08:49:00.051563+00:00, sha cf38b8bc341e)
  - site_page: https://swebench.com/SWE-bench (fetched 2026-08-29T08:49:00.060749+00:00, sha 93ad0eb83f44)
- Data as of 2026-08-30T08:39:29.467469+00:00.