Skip to main navigation menu Skip to main content Skip to site footer

Vol. 12 No. 09 (2025)
International Multidisciplinary Journal for Research & Development

Articles

ADAPTIVE AND EFFICIENT CODE-INTELLIGENCE: INTEGRATING LLM-GUIDED STATIC ANALYSIS, PERFORMANCE-AWARE GENERATION, AND SUSTAINABLE INFERENCE FOR GREEN SOFTWARE ENGINEERING

Published 2025-09-30

  • Dr. Kavya R. Menon

Dr. Kavya R. Menon
Meridian Institute of Technology, India

Abstract

This article synthesizes contemporary advances in large language model (LLM)-assisted code intelligence, situating recent breakthroughs in code generation, optimization, and inference-efficiency within a unified theoretical and practical framework. We present an integrative narrative that combines LLM-driven static analysis augmentation, iterative self-refinement of generated code, and system-level approaches for improving runtime performance and reducing environmental footprint. Drawing on empirical and methodological threads from recent literature, we articulate a conceptual methodology that couples: (1) LLM-augmented static analyzers for improved bug detection and maintainability (Li et al., 2024); (2) iterative refinement and execution-feedback loops to elevate correctness and performance (Madaan et al., 2023; Peng et al., 2024); (3) code-generation customization for domain-specific formalism such as TikZ and technical typesetting (Reux et al., 2025); and (4) inference and architectural optimizations—quantization, pruning, near-storage processing, and attention efficiency—to lower latency, memory, and energy costs (Ji Lin et al., 2023; Frantar et al., 2023; Jang, 2025). In addition, the article examines environmental metrics and policy considerations for green AI in the software engineering lifecycle (World Bank, 2024; Morand et al., 2024; ADEME, 2025). We propose a theoretical pipeline—Adaptive Efficient Code Intelligence (AECI)—and discuss its implications, potential pitfalls, and future research directions. The article makes no empirical claims beyond synthesizing and reinterpreting the provided references, but offers detailed operational prescriptions for researchers and practitioners seeking to combine correctness, performance, and sustainability in LLM-enabled software engineering.

Keywords

LLM code generation, static analysis, performance feedback

PDF

References

  1. DeepSeek-AI et al., “DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence,” arXiv, Jun. 2024.
  2. H. Li, Y. Hao, Y. Zhai, and Z. Qian, “Enhancing static analysis for practical bug detection: An llm-integrated approach,” Proc. ACM Program. Lang., vol. 8, no. OOPSLA1, Apr. 2024. Available: https://doi.org/10.1145/3649828
  3. C. Reux, M. Acher, D. E. Khelladi, O. Barais, and C. Quinton, “LLM Code Customization with Visual Results: A Benchmark on TikZ,” Proceedings of The 29th International Conference on Evaluation and Assessment in Software Engineering (EASE 2025)., Istanbul, Turkey, Jun. 2025. Available: https://hal.science/hal-05049250
  4. S. S. Dvivedi, V. Vijay, S. L. R. Pujari, S. Lodh, and D. Kumar, “A Comparative Analysis of Large Language Models for Code Documentation Generation,” Proceedings of the 1st ACM International Conference on AI-Powered Software (AIware 2024), Jul. 2024, pp. 65–73.
  5. T. Ye, W. Huang, X. Zhang, T. Ma, P. Liu, J. Yin, and W. Wang, “LLM4EFFI: Leveraging Large Language Models to Enhance Code Efficiency and Correctness,” arXiv, Feb. 2025.
  6. D. Huang, J. Dai, H. Weng, P. Wu, Y. Qing, H. Cui, Z. Guo, and J. M. Zhang, “EffiLearner: Enhancing Efficiency of Generated Code via Self-Optimization,” arXiv, May 2025.
  7. Y. Peng, A. D. Gotmare, M. Lyu, C. Xiong, S. Savarese, and D. Sahoo, “PerfCodeGen: Improving Performance of LLM Generated Code with Execution Feedback,” arXiv, Nov. 2024.
  8. Madaan, N. Tandon, P. Gupta, S. Hallinan, L. Gao, S. Wiegreffe, U. Alon, N. Dziri, S. Prabhumoye, Y. Yang, S. Welleck, B. P. Majumder, S. Gupta, A. Yazdanbakhsh, and P. Clark, “Self-Refine: Iterative Refinement with Self-Feedback,” arXiv, Mar. 2023.
  9. World Bank Group, Measuring the Emissions and Energy Footprint of the ICT Sector: Implications for Climate Action, Other Environmental Study. Washington, D.C: The World Bank, 2024.
  10. ADEME, “Numerique & environnement : Entre opportunit´es et n´ecessaire sobri´et´e,” Jan. 2025.
  11. Morand, A.-L. Ligozat, and A. Nev´eol, “How Green Can AI Be? A Study of Trends in Machine Learning Environmental Impacts,” arXiv, Dec. 2024.
  12. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is All You Need,” Advances in Neural Information Processing Systems 30 (NIPS 2017), Long Beach, CA, USA, 4–9 Dec. 2017. Available: https://arxiv.org/abs/1706.03762
  13. T. Dao, D. Fu, S. Ermon, A. Ré, and C. Ré, “FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness,” Advances in Neural Information Processing Systems 35 (NeurIPS 2022), New Orleans, LA, USA, 28 Nov.–9 Dec. 2022. Available: https://arxiv.org/abs/2205.14135
  14. Beltagy, M. E. Peters, and A. Cohan, “Longformer: The Long-Document Transformer,” arXiv, 2020. Available: https://arxiv.org/abs/2004.05150
  15. N. Kitaev, Ł. Kaiser, and A. Levskaya, “Reformer: The Efficient Transformer,” International Conference on Learning Representations (ICLR), 2020. Available: https://arxiv.org/abs/2001.04451
  16. Y. Tay, M. Dehghani, D. Bahri, and D. Metzler, “Efficient Transformers: A Survey,” arXiv, 2020. Available: https://arxiv.org/abs/2009.06732
  17. K. Choromanski, V. Likhosherstov, D. Dohan, X. Song, A. Gane, T. Sarlos, P. Hawkins, J. Davis, A. Mohiuddin, Ł. Kaiser, et al., “Rethinking Attention with Performers,” International Conference on Learning Representations (ICLR), 2021. Available: https://arxiv.org/abs/2009.14794
  18. Zhang, I. Titov, and R. Sennrich, “Sparse Attention with Linear Unit,” Proceedings of the ACL, Online, 7–11 Nov. 2021.
  19. Dr. K Naveen Kumar, "Open AI Model Efficient Memory Reduce Management for the Large Language Models," International Journal for Research in Applied Science and Engineering Technology, vol. 12, no. 5, pp. 1224-1231, 2023. https://www.ijraset.com/researchpaper/open-ai-model-efficient-memory-reduce-management-for-the-large-language-models
  20. Eelias Frantar et al., "Massive Language Models Can Be Accurately Pruned in One-Shot," Jan. 2023. https://www.researchgate.net/publication/366821751_Massive_Language_Models_Can_Be_Accurately_Pruned_in_One-Shot
  21. George Obaido et al., "XtremeLLMs: Towards Extremely Large Language Models," Preprints, 2023. https://www.preprints.org/manuscript/202408.1483/v1
  22. Hongsun Jang, "INF2: High-Throughput Generative Inference of Large Language Models using Near-Storage Processing," Feb. 2025. https://www.researchgate.net/publication/389056249_INF2_HighThroughput_Generative_Inference_of_Large_Language_Models_using_Near-Storage_Processing
  23. Ji Lin et al., "AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration," June 2023. https://www.researchgate.net/publication/371222812_AWQ_Activationaware_Weight_Quantization_for_LLM_Compression_and_Acceleration
  24. R. Chandra, "Reducing latency and enhancing accuracy in LLM inference through firmware-level optimization," International Journal of Signal Processing, Embedded Systems and VLSI Design, 5(2), 26-36, 2025.

Downloads

Download data is not yet available.