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A Systematic Literature Review of Parameter-Efficient Fine-Tuning for Large Code Models

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arxiv 2504.21569 v3 pith:XZYXKMK3 submitted 2025-04-29 cs.SE

Saima Afrin , Md Zahidul Haque , Antonio Mastropaolo This is my paper
classification cs.SE
keywords codemodelspeftfine-tuninglargereviewsoftwareadaptation
verification ladder T0 review T1 audit T2 compute T3 formal
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The rise of Artificial Intelligence (AI)-and particularly Large Language Models (LLMs) for code-has reshaped Software Engineering (SE) by enabling the automation of tasks such as code generation, bug detection, and repair. However, these models require significant computational resources for training and fine-tuning, posing challenges for real-world adoption in resource-constrained environments. To address this, the research community has increasingly turned to Parameter-Efficient Fine-Tuning (PEFT)-a class of techniques that enables the adaptation of large models by updating only a small subset of parameters, rather than the entire model. In this Systematic Literature Review (SLR), we examine the growing application of PEFT techniques-across a wide range of software engineering tasks. We analyze how these methods are used to optimize various deep learning (DL) architectures, focusing on their impact on both performance and efficiency. Our study synthesizes findings from 28 peer-reviewed papers, identifying patterns in configuration strategies and adaptation trade-offs. The outcome of this review is a comprehensive taxonomy that categorizes PEFT usage by task type, distinguishing between generative (e.g., Code Summarization) and non-generative (e.g., Code Clone Detection) scenarios. Our findings aim to inform future research and guide the practical deployment of PEFT in sustainable, AI-powered software development. Our artifacts are publicly available at https://github.com/alvi75/SLR-PEFT

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Parameter-Efficient Multi-Task Fine-Tuning in Code-Related Tasks

    cs.SE 2026-01 conditional novelty 5.0 of 10

    Multi-task QLoRA on Qwen2.5-Coder matches or beats single-task QLoRA and full fine-tuning for code generation and Python summarization, but lags in Java-to-C# translation.

  2. The Impact of Fine-tuning Large Language Models on Automated Program Repair

    cs.SE 2025-07 conditional novelty 5.0 of 10

    On three Java APR benchmarks, LoRA and IA3 adapters match or beat full-model fine-tuning for most tested code LLMs while training less than one percent of parameters.

  3. Gradient-Based Model Fingerprinting for LLM Similarity Detection and Family Classification

    cs.LG 2025-06 conditional novelty 5.0 of 10

    TensorGuard classifies fine-tuned LLMs into their base-model families with 94% accuracy by clustering statistical features of weight gradients under random input perturbations.

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