GSO: Challenging Software Optimization Tasks for Evaluating SWE-Agents
Authors: name: Manish Shetty, name: Naman Jain, name: Jinjian Liu, name: Vijay Kethanaboyina, name: Koushik Sen, name: Ion Stoica

Developing high-performance software is a complex task that requires
specialized expertise. We introduce GSO, a benchmark for evaluating language
models' capabilities in developing high-performance software. We develop an
automated pipeline that generates and executes performance tests to analyze
repository commit histories to identify 102 challenging optimization tasks
across 10 codebases, spanning diverse domains and programming languages. An
agent is provided with a codebase and performance test as a precise
specification, and tasked to improve the runtime efficiency, which is measured
against the expert developer optimization. Our quantitative evaluation reveals
that leading SWE-Agents struggle significantly, achieving less than 5% success
rate, with limited improvements even with inference-time scaling. Our
qualitative analysis identifies key failure modes, including difficulties with
low-level languages, practicing lazy optimization strategies, and challenges in
accurately localizing bottlenecks. We release the code and artifacts of our
benchmark along with agent trajectories to enable future research.