arXiv:2512.22827v2 Announce Type: replace-cross Abstract: Code often suffers from performance bugs. These bugs necessitate the research and practice of code optimization. Traditional rule-based methods rely on manually designing and maintaining rules for specific performance bugs (e.g., redundant loops, repeated computations), making them labor-intensive and limited in applicability. In recent years, machine learning and deep learning-based methods have emerged as promising alternatives by learning optimization heuristics from annotated code corpora and performance measurements. However, these
The rapid advancement of large language models (LLMs) provides the necessary computational capacity and understanding to address complex code optimization challenges that traditional methods could not effectively solve.
This development could significantly enhance software efficiency, reduce computational costs, and democratize access to high-performance coding practices, particularly for complex AI systems.
Code optimization, traditionally labor-intensive and rule-based, is becoming more automated, intelligent, and scalable through AI, potentially shifting development paradigms.
- · Software developers
- · Cloud computing providers
- · AI development firms
- · Large enterprises with complex codebases
- · Manual code optimization consultants
- · Legacy performance tools
- · Companies slow to adopt AI-driven development practices
Wider adoption of AI-powered development tools for performance enhancement.
Increased efficiency and lower operational costs for AI-heavy applications, accelerating AI deployment.
A potential shift in programming education towards understanding AI optimizers rather than solely manual optimization techniques.
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Read at arXiv cs.AI