arXiv subjects

Fred Lewis

Publications and source records attributed to Fred Lewis.

2 recordsLinked to original sources

arXiv2026

Characterizing the Quality Profile of AI-Generated C++ in Production

The widespread integration of AI coding assistants offers undeniable boosts to engineering velocity. Yet, recent studies point to a growing trade-off, revealing persistent challenges with code quality and maintainability. Industry leaders, including frontier AI labs, echo these concerns. As large language models are increasingly relied upon to author production code, understanding their impact on shipped software quality has become a critical priority. However, assessing these effects in industrial workflows remains difficult due to observability barriers. We study the impact of AI-generated code on production quality within a large enterprise operating global products relied upon by billions of users daily. Driven by this scale and user trust, the organization values code quality and has built thorough observability for every line of code deployed into production, enabling us to overcome measurement barriers to assess these effects. This study presents a large-scale empirical analysis of AI-generated C++ code from April 2025 to April 2026, tracking 3.52 million code changes across this enterprise's brownfield codebase. The core purpose is to understand the quality, performance, and maintenance characteristics of AI-generated code compared to human-written code in a production environment at scale. We find that AI-generated C++ code has a distinct quality profile, showing higher rates of interface and coupling burdens, copy and allocation overheads, and a reliance on explicit loops over optimized standard APIs. These issues translate into tangible downstream costs, including increased review effort and a 5-8% increase in compute resource consumption. However, we demonstrate that providing models with targeted, taxonomy-informed feedback can mitigate these effects, leading to an 11.1% reduction in targeted static analysis warnings and improved computational efficiency.

cs.SE↗

arXiv2025

ECO: An LLM-Driven Efficient Code Optimizer for Warehouse Scale Computers

With the end of Moore's Law, optimizing code for performance has become paramount for meeting ever-increasing compute demands, particularly in hyperscale data centers where even small efficiency gains translate to significant resource and energy savings. Traditionally, this process requires significant programmer effort to identify optimization opportunities, modify the code to implement the optimization, and carefully deploy and measure the optimization's impact. Despite a significant amount of work on automating program edits and promising results in small-scale settings, such performance optimizations have remained elusive in large real-world production environments, due to the scale, high degree of complexity, and reliability required. This paper introduces ECO (Efficient Code Optimizer), a system that automatically refactors source code to improve performance at scale. To achieve these performance gains, ECO searches through historical commits at scale to create a dictionary of performance anti-patterns that these commits addressed. These anti-patterns are used to search for similar patterns in a code base of billions of lines of code, pinpointing other code segments with similar potential optimization opportunities. Using a fine-tuned LLM, ECO then automatically refactors the code to generate and apply similar edits. Next, ECO verifies the transformed code, submits it for code review, and measures the impact of the optimization in production. Currently deployed on Google's hyperscale production fleet, this system has driven >25k changed lines of production code, across over 6.4k submitted commits, with a >99.5% production success rate. Over the past year, ECO has consistently resulted in significant performance savings every quarter. On average, the savings produced per quarter are equivalent to over 500k normalized CPU cores.

cs.SE↗