Code Review Automation Via Multi-task Federated LLM -- An Empirical Study

• URL: http://arxiv.org/abs/2412.15676v1
• Date: Fri, 20 Dec 2024 08:46:46 GMT
• Title: Code Review Automation Via Multi-task Federated LLM -- An Empirical Study
• Authors: Jahnavi Kumar, Sridhar Chimalakonda,
• Abstract summary: The study explores five simple techniques for multi-task training, including two sequential methods, one parallel method, and two cumulative methods.<n>The results indicate that sequentially training a federated LLM (FedLLM) for our code review multi-task use case is less efficient in terms of time, computation, and performance metrics, compared to training separate models for each task.
• Score: 4.8342038441006805
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Code review is a crucial process before deploying code to production, as it validates the code, provides suggestions for improvements, and identifies errors such as missed edge cases. In projects with regular production releases, the effort required for peer code-reviews remains high. Consequently, there has been significant interest from software engineering (SE) researchers in automating the code review process. Previous research on code review automation has typically approached the task as three independent sub-tasks: review necessity prediction, review comment generation, and code refinement. Our study attempts to (i) leverage the relationships between the sub-tasks of code review automation, by developing a multi-task model that addresses all tasks in an integrated manner, and (ii) increase model robustness on unseen data via collaborative large language model (LLM) modeling, while retaining the proprietary nature of code, by using federated learning (FL). The study explores five simple techniques for multi-task training, including two sequential methods, one parallel method, and two cumulative methods. The results indicate that sequentially training a federated LLM (FedLLM) for our code review multi-task use case is less efficient in terms of time, computation, and performance metrics, compared to training separate models for each task. Because sequential training demonstrates catastrophic forgetting, alternatively cumulative fine-tuning for multi-task training performs better than training models for individual tasks. This study highlights the need for research focused on effective fine-tuning of multi-task FedLLMs for SE tasks.

Related papers

• Single-Input Multi-Output Model Merging: Leveraging Foundation Models for Dense Multi-Task Learning [46.51245338355645]
  Model merging is a flexible and computationally tractable approach to merge single-task checkpoints into a multi-task model. We show that it qualitatively differs from the single-input-multiple-output model merging settings studied in the literature due to the existence of task-specific decoders. We propose two simple and efficient fixes for the SIMO setting to re-align the feature representation after merging.
  arXiv  Detail & Related papers  (2025-04-15T15:10:46Z)
• Should Code Models Learn Pedagogically? A Preliminary Evaluation of Curriculum Learning for Real-World Software Engineering Tasks [2.0072624123275533]
  Recent work shows that Curriculum Learning can improve performance on code-related tasks through incremental learning based on the difficulty of synthetic code. We investigate how the pre-trained code model (CodeT5) learns under CL, through the tasks of code clone detection and code summarization. Our empirical study on the CodeXGLUE benchmark showed contrasting results to prior studies, where the model exhibited signs of catastrophic forgetting and shortcut learning.
  arXiv  Detail & Related papers  (2025-02-06T06:33:08Z)
• Improving the Learning of Code Review Successive Tasks with Cross-Task Knowledge Distillation [1.0878040851638]
  We introduce a novel deep-learning architecture, named DISCOREV, which employs cross-task knowledge distillation to address these tasks simultaneously. We show that our approach generates better review comments, as measured by the BLEU score, as well as more accurate code refinement according to the CodeBLEU score.
  arXiv  Detail & Related papers  (2024-02-03T07:02:22Z)
• AdaMerging: Adaptive Model Merging for Multi-Task Learning [68.75885518081357]
  This paper introduces an innovative technique called Adaptive Model Merging (AdaMerging) It aims to autonomously learn the coefficients for model merging, either in a task-wise or layer-wise manner, without relying on the original training data. Compared to the current state-of-the-art task arithmetic merging scheme, AdaMerging showcases a remarkable 11% improvement in performance.
  arXiv  Detail & Related papers  (2023-10-04T04:26:33Z)
• CodeCoT: Tackling Code Syntax Errors in CoT Reasoning for Code Generation [6.139760107605468]
  Chain-of-thought (CoT) has emerged as a groundbreaking tool in NLP, notably for its efficacy in complex reasoning tasks. We present Code Chain-of-Thought (CodeCoT) that integrates CoT with a self-examination process for code generation.
  arXiv  Detail & Related papers  (2023-08-17T04:58:51Z)
• Diffusion Model is an Effective Planner and Data Synthesizer for Multi-Task Reinforcement Learning [101.66860222415512]
  Multi-Task Diffusion Model (textscMTDiff) is a diffusion-based method that incorporates Transformer backbones and prompt learning for generative planning and data synthesis. For generative planning, we find textscMTDiff outperforms state-of-the-art algorithms across 50 tasks on Meta-World and 8 maps on Maze2D.
  arXiv  Detail & Related papers  (2023-05-29T05:20:38Z)
• Pre-training Multi-task Contrastive Learning Models for Scientific Literature Understanding [52.723297744257536]
  Pre-trained language models (LMs) have shown effectiveness in scientific literature understanding tasks. We propose a multi-task contrastive learning framework, SciMult, to facilitate common knowledge sharing across different literature understanding tasks.
  arXiv  Detail & Related papers  (2023-05-23T16:47:22Z)
• Identification of Negative Transfers in Multitask Learning Using Surrogate Models [29.882265735630046]
  Multitask learning is widely used to train a low-resource target task by augmenting it with multiple related source tasks. A critical problem in multitask learning is identifying subsets of source tasks that would benefit the target task. We introduce an efficient procedure to address this problem via surrogate modeling.
  arXiv  Detail & Related papers  (2023-03-25T23:16:11Z)
• On Steering Multi-Annotations per Sample for Multi-Task Learning [79.98259057711044]
  The study of multi-task learning has drawn great attention from the community. Despite the remarkable progress, the challenge of optimally learning different tasks simultaneously remains to be explored. Previous works attempt to modify the gradients from different tasks. Yet these methods give a subjective assumption of the relationship between tasks, and the modified gradient may be less accurate. In this paper, we introduce Task Allocation(STA), a mechanism that addresses this issue by a task allocation approach, in which each sample is randomly allocated a subset of tasks. For further progress, we propose Interleaved Task Allocation(ISTA) to iteratively allocate all
  arXiv  Detail & Related papers  (2022-03-06T11:57:18Z)
• In Defense of the Unitary Scalarization for Deep Multi-Task Learning [121.76421174107463]
  We present a theoretical analysis suggesting that many specialized multi-tasks can be interpreted as forms of regularization. We show that, when coupled with standard regularization and stabilization techniques, unitary scalarization matches or improves upon the performance of complex multitasks.
  arXiv  Detail & Related papers  (2022-01-11T18:44:17Z)
• Multi-Task Learning with Sequence-Conditioned Transporter Networks [67.57293592529517]
  We aim to solve multi-task learning through the lens of sequence-conditioning and weighted sampling. We propose a new suite of benchmark aimed at compositional tasks, MultiRavens, which allows defining custom task combinations. Second, we propose a vision-based end-to-end system architecture, Sequence-Conditioned Transporter Networks, which augments Goal-Conditioned Transporter Networks with sequence-conditioning and weighted sampling.
  arXiv  Detail & Related papers  (2021-09-15T21:19:11Z)
• Memory-Based Optimization Methods for Model-Agnostic Meta-Learning and Personalized Federated Learning [56.17603785248675]
  Model-agnostic meta-learning (MAML) has become a popular research area. Existing MAML algorithms rely on the episode' idea by sampling a few tasks and data points to update the meta-model at each iteration. This paper proposes memory-based algorithms for MAML that converge with vanishing error.
  arXiv  Detail & Related papers  (2021-06-09T08:47:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.