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.

The rapid growth in the use of Large Language Models (LLMs) and AI Agents as part of software development and deployment is revolutionizing the information technology landscape. While code generation receives significant attention, a higher-impact application lies in using agents for the operational resilience of cloud services, which currently require significant human effort and domain knowledge. There is a growing interest in AI for IT Operations (AIOps), which aims to automate complex operational tasks, like fault localization and root cause analysis, reducing human intervention and customer impact. However, achieving the vision of autonomous and self-healing clouds through AIOps is hampered by the lack of standardized frameworks for building, evaluating, and improving AIOps agents. This vision paper lays the groundwork for such a framework by framing the requirements and then discussing design decisions that satisfy them. We also propose AIOpsLab, a prototype implementation leveraging agent-cloud-interface that orchestrates an application, injects real-time faults using chaos engineering, and interfaces with an agent to localize and resolve the faults. We report promising results and lay the groundwork to build a modular and robust framework for building, evaluating, and improving agents for autonomous clouds.

While Large Language Models’ (LLMs) coding capabilities have advanced rapidly, corresponding evaluation benchmarks on real-world programming setups are yet to catch up. Building a scalable and interactive testbed for evaluating general-purpose AI coding agents for real-world code has been challenging, particularly due to a lack of high-quality test suites available. In this paper, we present Repository to Environment (R2E), a framework that can turn any GitHub repository into a test environment to evaluate the performance of code-generating systems, both static and interactive. R2E is powered by a synergistic combination of program analysis and LLMs to construct equivalence test harnesses for any GitHub function. We instantiate our framework to build the first large-scale benchmark, R2E-Eval1, for building realistic environments for AI coding assistants. Our results demonstrate that even when SOTA models cannot generate correct solutions with advanced prompting techniques, they can effectively use environment feedback highlighting the need to move from static functional coding to interactive programming paradigm. We hope that our framework (and the instantiated benchmark) can motivate research directions by providing web-scale open-ended coding environments. R2E code is available at https://r2e.dev/

Production incidents in today's large-scale cloud services can be extremely expensive in terms of customer impacts and engineering resources required to mitigate them. Despite continuous reliability efforts, cloud services still experience severe incidents due to various root-causes. Worse, many of these incidents last for a long period as existing techniques and practices fail to quickly detect and mitigate them. To better understand the problems, we carefully study hundreds of recent high severity incidents and their postmortems in Microsoft-Teams, a large-scale distributed cloud based service used by hundreds of millions of users. We answer: (a) why the incidents occurred and how they were resolved, (b) what the gaps were in current processes which caused delayed response, and (c) what automation could help make the services resilient. Finally, we uncover interesting insights by a novel multi-dimensional analysis that correlates different troubleshooting stages (detection, root-causing and mitigation), and provide guidance on how to tackle complex incidents through automation or testing at different granularity.

Crash localization, an important step in debugging crashes, is challenging when dealing with an extremely large number of diverse applications and platforms and underlying root causes. Large-scale error reporting systems, e.g., Windows Error Reporting (WER), commonly rely on manually developed rules and heuristics to localize blamed frames causing the crashes. As new applications and features are routinely introduced and existing applications are run under new environments, developing new rules and maintaining existing ones become extremely challenging.

Incident management is a key aspect of operating large-scale cloud services. To aid with faster and efficient resolution of incidents, engineering teams document frequent troubleshooting steps in the form of Troubleshooting Guides (TSGs), to be used by on-call engineers (OCEs). However, TSGs are siloed, unstructured, and often incomplete, requiring developers to manually understand and execute necessary steps. This results in a plethora of issues such as on-call fatigue, reduced productivity, and human errors. In this work, we conduct a large-scale empirical study of over 4K+ TSGs mapped to incidents and find that TSGs are widely used and help significantly reduce mitigation efforts. We then analyze feedback on TSGs provided by 400+ OCEs and propose a taxonomy of issues that highlights significant gaps in TSG quality. To alleviate these gaps, we investigate the automation of TSGs and propose AutoTSG -- a novel framework for automation of TSGs to executable workflows by combining machine learning and program synthesis. Our evaluation of AutoTSG on 50 TSGs shows the effectiveness in both identifying TSG statements (accuracy 0.89) and parsing them for execution (precision 0.94 and recall 0.91). Lastly, we survey ten Microsoft engineers and show the importance of TSG automation and the usefulness of AutoTSG.