Language Models for Code Optimization: Survey, Challenges and Future Directions (2501.01277v1)

Abstract: LLMs (LMs) built upon deep neural networks (DNNs) have recently demonstrated breakthrough effectiveness in software engineering tasks like code generation, code completion, and code repair. This has paved the way for the emergence of LM-based code optimization techniques, which are pivotal for enhancing the performance of existing programs, such as accelerating program execution time. However, a comprehensive survey dedicated to this specific application has been lacking. To address this gap, we present a systematic literature review of over 50 primary studies, identifying emerging trends and addressing 11 specialized questions. The results disclose five critical open challenges, such as balancing model complexity with practical usability, enhancing generalizability, and building trust in AI-powered solutions. Furthermore, we provide eight future research directions to facilitate more efficient, robust, and reliable LM-based code optimization. Thereby, this study seeks to provide actionable insights and foundational references for both researchers and practitioners in this rapidly evolving field.

Overview of "LLMs for Code Optimization: Survey, Challenges and Future Directions"

The paper, titled "LLMs for Code Optimization: Survey, Challenges and Future Directions," presents a comprehensive survey of the utilization of LLMs (LMs), particularly those based on Deep Neural Networks (DNNs), for code optimization tasks. This overview highlights the core areas and significant findings from the paper, critically assesses the methodologies employed, and suggests potential directions for future research.

Key Concepts and Methodologies

Code optimization is crucial in enhancing software performance by transforming programs to meet specific goals such as reduced execution time, minimized code size, or optimized memory usage. Traditional techniques in this domain have relied heavily on heuristic-driven strategies and compiler optimizations. However, the advent of LMs has revolutionized the landscape, demonstrating impressive results in tasks like code generation, completion, and repair.

The paper systematically reviews over 50 studies, categorizing them based on characteristics of LMs leveraged, the challenges they address, and methodologies employed. A significant portion of the review focuses on understanding how these models have been adapted for code optimization, detailing aspects such as the nature of pre-trained models, the size and complexity of models used, and specific application areas.

Core Challenges in Applying LLMs

Five salient challenges in using LMs for code optimization are outlined in the paper:

1. Model Complexity vs. Usability: As models grow in size, their practical usability diminishes due to increased computational resources required. This necessitates strategies for balancing complexity with efficiency.
2. Generalizability: LMs often struggle with generalizing optimizations across diverse codebases and computational environments.
3. Trust and Transparency: Building trust in LM-driven solutions remains challenging due to issues like hallucination and performance inconsistencies.
4. Integration with External Systems: Effective code optimization often requires interaction with external systems and datasets, which remains an underexplored area.
5. Evaluation in Real-World Scenarios: The gap between theoretical capabilities and practical application is significant, with many studies relying on synthetic benchmarks rather than real-world data environments.

Methodological Insights

The paper demonstrates that existing research primarily focuses on leveraging pre-trained LMs to improve code performance. Studies often employ feedback-based iterative approaches, agentic workflows, and prompt engineering to refine and enhance model outputs. These methods, while effective in controlled settings, highlight the dependence on sophisticated model architectures and the need for in-depth evaluation metrics that capture multiple dimensions of optimization beyond runtime efficiency.

Future Directions

The paper advocates several future research directions:

• Model Compression and Ensembling: These techniques are suggested to address the challenge of reconciling model complexity with practical deployment, focusing on maintaining accuracy while reducing computational overhead.
• Cross-Domain Generalization: Strategies to enable LMs to adapt optimizations across different languages and environments are critical for broader applicability.
• Development of Real-World Benchmarks: Establishing comprehensive benchmarks involving real-world software projects could bridge the gap between experimental setups and practical applications.
• Advancing Human-AI Collaboration: By integrating human insights with the raw computational power of LMs, a synergistic approach can be developed to enhance reliability and acceptance.

Conclusion

This paper is a profound resource for researchers and practitioners aiming to explore the intersection of machine learning and software engineering. By outlining current capabilities, limitations, and future potential of LMs in code optimization, it sets the stage for advancing this rapidly evolving field. Researchers are encouraged to develop innovative approaches that address highlighted challenges, ensuring that LM-based optimization is both practical and impactful in real-world software engineering scenarios.