Copper: Unifying Correctness and Performance Specification in Code Generation

2026-07-03

AI Summary

Existing AI-generated code often struggles to simultaneously achieve functional correctness and runtime performance. To address this challenge, this work proposes Copper, a novel framework that, for the first time, integrates formal correctness guarantees with performance optimization objectives within an AI-assisted programming pipeline. Copper establishes a closed-loop optimization mechanism by synergistically combining AI-driven code synthesis, formal verification, automated performance profiling, and performance-aware specifications. Experimental results demonstrate that, across diverse algorithms and real-world programming tasks, Copper consistently produces code that not only rigorously satisfies functional correctness but also significantly outperforms state-of-the-art AI baselines in both execution time and memory efficiency.

Abstract

Generative AI has made remarkable progress in producing functionally correct code, yet ensuring both correctness and performance remains an open challenge. We present Copper, a framework that combines formal verification with performance-aware specification to generate code that is provably correct and efficiently executable. Our approach integrates AI-driven code synthesis with formal verification tools, and automated performance profiling loops. Evaluated on a diverse set of algorithmic and real-world programming tasks, Copper produces solutions that satisfy strict correctness guarantees while delivering significant improvements in runtime and memory efficiency compared to baseline AI-generated code. This work demonstrates that it is feasible to bridge the gap between trustworthiness and performance in AI-assisted programming, offering a practical pathway toward reliable, high-performance code generation.

Problem

• code generation
• correctness
• performance
• AI-assisted programming

Innovation

• formal verification
• performance-aware specification
• AI-driven code synthesis
• automated performance profiling
• correctness guarantees