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Code Summarization without Direct Access to Code - Towards Exploring Federated LLMs for Software Engineering

Authors:

Sridhar ChimalakondaAuthors Info & Claims

EASE '24: Proceedings of the 28th International Conference on Evaluation and Assessment in Software Engineering

Pages 100 - 109

https://doi.org/10.1145/3661167.3661210

Published: 18 June 2024 Publication History

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Abstract

Software Engineering (SE) researchers are extensively applying Large Language Models (LLMs) to address challenges in SE tasks such as code clone detection, code summarization, and program comprehension. Despite promising results, LLMs have to be fine-tuned and customized with specific datasets for optimal performance. However, the proprietary nature of SE data, and the lack of LLMs trained on non-open source data is an open problem. While there exists work on applying Federated Learning (FL) for SE, integration of FL with LLMs for SE is unexplored. Hence, we propose a FedLLM for “code summarization” as developers spend more time in comprehending code. We setup a federated learning architecture and fine-tune LLM (Llama2 with 6.7B parameters) using Parameter Efficient Fine-Tuning (PEFT) for code summarization. We conducted our experiments on 40GB RAM GPU in an A100 architecture. Results show that FL-trained LLM is as effective as a centrally-trained one. We envision that leveraging non-open source data using FedLLM for SE could be an interesting research direction.

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Cited By

Zhang XHou XQiao XSong W(2024)A review of automatic source code summarizationEmpirical Software Engineering10.1007/s10664-024-10553-629:6Online publication date: 7-Oct-2024
https://dl.acm.org/doi/10.1007/s10664-024-10553-6
Jiang ZWang DRao D(2024)Leveraging meta-data of code for adapting prompt tuning for code summarizationApplied Intelligence10.1007/s10489-024-06197-055:3Online publication date: 23-Dec-2024
https://dl.acm.org/doi/10.1007/s10489-024-06197-0

Index Terms

Code Summarization without Direct Access to Code - Towards Exploring Federated LLMs for Software Engineering
1. Software and its engineering
  1. Software notations and tools
    1. Development frameworks and environments

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EASE '24: Proceedings of the 28th International Conference on Evaluation and Assessment in Software Engineering

June 2024

728 pages

ISBN:9798400717017

DOI:10.1145/3661167

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EASE 2024: 28th International Conference on Evaluation and Assessment in Software Engineering

June 18 - 21, 2024

Salerno, Italy

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Cited By

Zhang XHou XQiao XSong W(2024)A review of automatic source code summarizationEmpirical Software Engineering10.1007/s10664-024-10553-629:6Online publication date: 7-Oct-2024
https://dl.acm.org/doi/10.1007/s10664-024-10553-6
Jiang ZWang DRao D(2024)Leveraging meta-data of code for adapting prompt tuning for code summarizationApplied Intelligence10.1007/s10489-024-06197-055:3Online publication date: 23-Dec-2024
https://dl.acm.org/doi/10.1007/s10489-024-06197-0

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