8 citations · 15 across the 16 of their papers we have counts for
6 papers · 1 filter
Characterizing the Quality Profile of AI-Generated C++ in Production
Michael Tran, Fred Lewis, Kun Yang +5
The widespread integration of AI coding assistants offers undeniable boosts to engineering velocity. Yet, recent studies point to a growing trade-off, revealing persistent challeng…
Customizing an LLM for Enterprise Software Engineering
Aditya Kini, Satish Chandra, Milad Hashemi +15
Enterprise software development is a continuous evolutionary process, characterized by incremental additions, architectural revisions, production deployments and rigorous maintenan…
A Multi-agent AI System for Deep Learning Model Migration from TensorFlow to JAX
Stoyan Nikolov, Bernhard Konrad, Moritz Gronbach +5
The rapid development of AI-based products and their underlying models has led to constant innovation in deep learning frameworks. Google has been pioneering machine learning usage…
SWE-fficiency: Can Language Models Optimize Real-World Repositories on Real Workloads?
Jeffrey Jian Ma, Milad Hashemi, Amir Yazdanbakhsh +5
Optimizing the performance of large-scale software repositories demands expertise in code reasoning and software engineering (SWE) to reduce runtime while preserving program correc…
Instruction Set Migration at Warehouse Scale
Eric Christopher, Kevin Crossan, Wolff Dobson +7
Migrating codebases from one instruction set architecture (ISA) to another is a major engineering challenge. A recent example is the adoption of Arm (in addition to x86) across the…
ECO: An LLM-Driven Efficient Code Optimizer for Warehouse Scale Computers
Hannah Lin, Martin Maas, Maximilian Roquemore +17
This paper introduces ECO, a system that automatically modifies source code to improve performance at scale. ECO overcomes the localization problem by combining fleet-wide continuo…