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Language Models for Code Optimization: Survey, Challenges and Future Directions

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arxiv 2501.01277 v2 pith:J3W6AFHA submitted 2025-01-02 cs.SE

Language Models for Code Optimization: Survey, Challenges and Future Directions

Jingzhi Gong , Vardan Voskanyan , Paul Brookes , Fan Wu , Wei Jie , Jie Xu , Rafail Giavrimis , Mike Basios
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Leslie Kanthan Zheng Wang
This is my paper
classification cs.SE
keywords codeoptimizationchallengesdirectionsfuturelanguagelm-basedmodels
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Language models (LMs) built upon deep neural networks (DNNs) have recently demonstrated breakthrough effectiveness in software engineering tasks such as code generation, completion, and repair. This has paved the way for the emergence of LM-based code optimization techniques, which are crucial 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 fill this gap, we present a systematic literature review of over 50 primary studies, identifying emerging trends and addressing 11 specialized questions. Our findings reveal five critical open challenges, such as balancing model complexity with practical usability, cross-language/performance generalizability, and building trust in AI-driven solutions. Furthermore, we provide eight future research directions to facilitate more efficient, robust, and reliable LM-based code optimization. Thereby, this study aims to provide actionable insights and foundational references for both researchers and practitioners in this rapidly evolving field.

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Cited by 4 Pith papers

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    JETO-Mine is a reusable three-phase pipeline that mines 1.8 million Java commits to produce JETO-Bench containing 91 verified executable ETIPs, on which OpenHands succeeds at 14.3%.

  3. Lean Refactor: Multi-Objective Controllable Proof Optimization via Agentic Strategy Search

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    Lean Refactor uses retrieval from a curated multi-objective strategy database to guide frozen LLMs in refactoring Lean proofs, reporting over 70% token compression on benchmarks and improved version transfer.

  4. SysLLMatic: Large Language Models are Software System Optimizers

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    SysLLMatic integrates LLMs with performance diagnostics and a 43-pattern catalog to optimize complex software, reporting 1.54x latency and 1.24x energy gains over compilers on large Java systems where prior LLM method...