Tootfinder

GSO: Challenging Software Optimization Tasks for Evaluating SWE-Agents
Manish Shetty, Naman Jain, Jinjian Liu, Vijay Kethanaboyina, Koushik Sen, Ion Stoica
https://arxiv.org/abs/2505.23671

GSO: Challenging Software Optimization Tasks for Evaluating SWE-Agents
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 …

LinkAnchor: An Autonomous LLM-Based Agent for Issue-to-Commit Link Recovery
Arshia Akhavan, Alireza Hosseinpour, Abbas Heydarnoori, Mehdi Keshani
https://arxiv.org/abs/2508.12232

LinkAnchor: An Autonomous LLM-Based Agent for Issue-to-Commit Link Recovery
Issue-to-commit link recovery plays an important role in software traceability and improves project management. However, it remains a challenging task. A study on GitHub shows that only 42.2% of the issues are correctly linked to their commits. This highlights the potential for further development and research in this area. Existing studies have employed various AI/ML-based approaches, and with the recent development of large language models, researchers have leveraged LLMs to tackle this probl…

Industrial LLM-based Code Optimization under Regulation: A Mixture-of-Agents Approach
Mari Ashiga, Vardan Voskanyan, Fateme Dinmohammadi, Jingzhi Gong, Paul Brookes, Matthew Truscott, Rafail Giavrimis, Mike Basios, Leslie Kanthan, Wei Jie
https://arxiv.org/abs/2508.03329

Industrial LLM-based Code Optimization under Regulation: A Mixture-of-Agents Approach
Recent advancements in Large Language Models (LLMs) for code optimization have enabled industrial platforms to automate software performance engineering at unprecedented scale and speed. Yet, organizations in regulated industries face strict constraints on which LLMs they can use - many cannot utilize commercial models due to data privacy regulations and compliance requirements, creating a significant challenge for achieving high-quality code optimization while maintaining cost-effectiveness. W…

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GSO: Challenging Software Optimization Tasks for Evaluating SWE-Agents
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 …