Large Language Models for Code Summarization

Authors: Balázs Szalontai / Gergő Szalay / Tamás Márton / Anna Sike / Balázs Pintér / Tibor Gregorics

Year: 2024

Source: https://arxiv.org/abs/2405.19032

TLDR:

This technical report explores the performance of Large Language Models (LLMs) in code summarization and generation tasks. It reviews the capabilities of open-source LLMs such as CodeLlama, WizardCoder, and OctoCoder in synthesizing and explaining code based on natural language descriptions. The report discusses benchmarking metrics and datasets used to measure the performance of LLMs in code generation and explanation. It also highlights the importance of Encoder-Decoder architectures in natural language processing and their application to software engineering tasks. The report emphasizes the significance of LLMs in code summarization and generation, particularly in understanding legacy code and creating documentation. It discusses the use of metrics such as Pass@k, BLEU, and ROUGE for evaluating the performance of LLMs on different benchmark datasets. Additionally, it provides insights into specific LLMs and their performance on benchmarks such as HumanEval, APPS, MBPP, and DS-1000. Furthermore, the report delves into the HumanEvalExplain benchmark, which aims to determine the code explanation capabilities of LLMs. It discusses the challenges and opportunities in evaluating LLMs' capabilities in handling source code in relation to natural language text. The report concludes by summarizing the results and performance of various LLMs on different benchmarks, shedding light on their capabilities and limitations in code summarization and generation tasks.

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The technical report investigates the performance of Large Language Models (LLMs) in code summarization and generation tasks, reviewing their capabilities in synthesizing and explaining code based on natural language descriptions, and evaluating their performance on various benchmark datasets and metrics.

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Abstract

Recently, there has been increasing activity in using deep learning for software engineering, including tasks like code generation and summarization. In particular, the most recent coding Large Language Models seem to perform well on these problems. In this technical report, we aim to review how these models perform in code explanation/summarization, while also investigating their code generation capabilities (based on natural language descriptions).

Method

The method of this technical report involves reviewing the performance of open-source Large Language Models (LLMs) in code explanation/summarization and code generation tasks, focusing on their capabilities in handling source code in relation to natural language text. The report evaluates the performance of various LLMs on benchmark datasets such as HumanEval, APPS, MBPP, and DS-1000, and discusses the metrics used for measuring their performance, including Pass@k, BLEU, and ROUGE. Additionally, the report reviews specific LLMs such as CodeLlama, WizardCoder, and DeepSeekCoder, and their capabilities in synthesizing and explaining code. The report also delves into the evaluation of LLMs' code generation and summarization/explanation capabilities, providing insights into their performance on different benchmark tasks and datasets.

Main Finding

The main finding of this technical report is the comprehensive review of the performance of open-source Large Language Models (LLMs) in code summarization and generation tasks, with a focus on their capabilities in synthesizing and explaining code based on natural language descriptions. The report evaluates the performance of various LLMs on benchmark datasets such as HumanEval, APPS, MBPP, and DS-1000, and discusses the metrics used for measuring their performance, including Pass@k, BLEU, and ROUGE. Additionally, it highlights the challenges and opportunities in evaluating LLMs' capabilities in handling source code in relation to natural language text, shedding light on their capabilities and limitations in code summarization and generation tasks.

Conclusion

The conclusion of this technical report is a comprehensive review of the performance of open-source Large Language Models (LLMs) in code summarization and generation tasks. The report evaluates the capabilities of various LLMs in synthesizing and explaining code based on natural language descriptions, as well as their code generation capabilities. It discusses the results of benchmarking datasets such as HumanEval, APPS, MBPP, and DS-1000, and the metrics used for measuring their performance, including Pass@k, BLEU, and ROUGE. The report highlights the challenges and opportunities in evaluating LLMs' capabilities in handling source code in relation to natural language text, shedding light on their capabilities and limitations in code summarization and generation tasks. Additionally, it provides insights into specific LLMs such as CodeLlama, WizardCoder, and DeepSeekCoder, and their performance on various benchmark tasks and datasets. Overall, the report aims to provide a comprehensive understanding of the performance of LLMs in code summarization and generation tasks, emphasizing their capabilities and limitations in handling source code in relation to natural language text.

Keywords

Large Language Models, Code generation, Code explanation

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