Tiktok investigators launches SWE-PERK: The first benchmark of using the code operating code
Introduction
Like major languages of Language (LLMS) Advance in software engineering activities – from the production of codes to the Bug Fix-Performation optimization operations remaining Optive Frontier, especially at the last level. Closing this gap, researchers from Tiktok and interaction centers are presented Swe-prepzaThe first Benchmark is specially designed to test the llms's ability to perform the code performance in real repostories in the world.
Unlike the previous benchmarks focused on food or work performance (eg provides a renewable basis, learning and improving the efficient power of today's llms.
Why is SWE-Prech need
The Real-World World Codes are usually great, familiar, and very dependent. Preparation to work require a comprehension of file coordination, murder measures, and bottles collection – challenges more than reliability of Lolated Data Activity.
Llms today is closely tested on tasks such as synntax repairs or conversion of small functions. But in production areas, the operating system of reposingories can produce the best interests of the program. SWE-Fever is clearly built to measure the LLM skills in these settings.
Data construction
SWE-Femp is made up of more than 100,000 items pull applications on high profile Guthub. The last data-covered data is covered with 9 repositories including:
- The selected 140 conditions indicating the development of measurement and stability.
- Finish the code Before and Post-Actimization.
- Target activities divided as oracle (file-level) or literal (repo-level).
- Unit tests and docker areas the re-production of production and performance.
- Clips authorized by expert used as gold values.
To ensure legality, each of the data test must:
- Pass before and after a patch.
- Express an important time for more recycling time (Mann-Whitney Vivit, P <0.1).
The operation is estimated using minor operation (Δ), a mathematical division caused by a patch during the patch while filing the sound.
Benchmark settings: Oracle vs. Truth
- Oracle settings: The model receives only target activities and corresponding files. This is set to test the work done in the area.
- Practical setting: The model is given to the last place all and should identify and increase sensitive ways to work confidently. This is the closest analog for the engineers how do people work.
Test metrics
SWE-Freff describes three tier test framework, reporting for each metric independence:
- Claim: Is the modeling patch produced by cleaning?
- Accuracy: Does the patch maintain effective integrity (all of the unit test passing)?
- Performance: Did the patch reveal the development of the measurement time?
Metrics are not included in one point, allowing a sales-related testing between Syntactic accuracy and operational benefits.
Test results
Benchmark checks high quality llms under both Oracle Settings and practical settings:
| Statue | Set | Working (%) |
|---|---|---|
| Claude-4-opus | Aspect | 1.28 |
| GPT-4O | Aspect | 0.60 |
| Gemini-2.5-pro | Aspect | 1.48 |
| Claude-3.7 (Aferentent) | Truthful | 0.41 |
| Claude-3.7 (Openhands) | Truthful | 2.26 |
| Scholar (patch human) | – | 10.85 |
Significantly, even the best of LLM edits is the best of the collapse of people. The method of agent-based New Openhands, built on Claude-3.7-Sonnet, trigger other configurations in a reality but remains reduced after maintenance.
Important observation
- Agent-based frames such as Openhands Properly suited to useful use, which contains many steps, the best model model and pipeline methods such as Agentless.
- Working is polluting As the number of target activities rises-llms fight the broad monkeys of performing efficiency.
- Llms indicates limited measurement In the long last conditions, where the scholar systems continue to show work benefits.
- Patch analysis Displays the focus of the LLMS on top of the low-level code structures (eg internalization, environmental setting), and experts intend the appearance of high performance.
Store
SWE-Freff represents an important role in measuring and improving llms software in software Engineering FowerFows. It includes an important gap of the capacity of existing power between existing models and human professionals, providing a solid basis for future research in other limited performance. As the llms appears, SWE-Prev can serve as a northern star leading to a practical software development, which is prepared for a scale.
Look Page, GitHub page and project. All credit for this study goes to research for this project.
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