Source-Code Summarization Task
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A Source-Code Summarization Task is a linguistic generation task that involves the automatic generation of concise natural language descriptions (summaries) for pieces of source code. This task is crucial in software engineering for enhancing program comprehension, documentation, and maintenance.
- Context:
- It can (typically) involve the use of Machine Learning and Deep Learning models to analyze and summarize code snippets.
- It can (often) require the input of a Code Snippet to generate a concise Code Summary.
- It can range from using Rule-Based Approaches to advanced Deep Learning Approaches for generating summaries.
- It can improve the readability and understandability of code by providing clear and concise explanations of code functionality.
- It can facilitate code review processes by offering quick overviews of code snippets.
- It can assist in various software maintenance activities by providing accurate and up-to-date code documentation.
- It can be evaluated using Automated Metrics such as BLEU, METEOR, and ROUGE, as well as through Human Evaluation.
- It can employ techniques like Sequence-to-Sequence Models for generating summaries.
- It can be applied in different programming languages, with performance varying across languages like Java, Python, and C.
- It can involve Prompting Techniques like zero-shot and few-shot learning for LLMs.
- ...
- Example(s):
- a Python Code Summarization task that generates summaries for Python functions and classes.
- a Java Code Summarization task that creates descriptions for Java methods and objects.
- a C Code Summarization task that explains the functionality of C functions and structures.
- ...
- Counter-Example(s):
- See: Machine Learning, Deep Learning, Code Review, Software Documentation.
References
2024
- (Sun et al., 2024) ⇒ Weisong Sun, Yun Miao, Yuekang Li, Hongyu Zhang, Chunrong Fang, Yi Liu, Gelei Deng, Yang Liu, and Zhenyu Chen. (2024). “Source Code Summarization in the Era of Large Language Models.” arXiv preprint arXiv:2407.07959
- ABSTRACT: To support software developers in understanding and maintaining programs, various automatic (source) code summarization techniques have been proposed to generate a concise natural language summary (i.e., comment) for a given code snippet. Recently, the emergence of large language models (LLMs) has led to a great boost in the performance of code-related tasks. In this paper, we undertake a systematic and comprehensive study on code summarization in the era of LLMs, which covers multiple aspects involved in the workflow of LLM-based code summarization. Specifically, we begin by examining prevalent automated evaluation methods for assessing the quality of summaries generated by LLMs and find that the results of the GPT-4 evaluation method are most closely aligned with human evaluation. Then, we explore the effectiveness of five prompting techniques (zero-shot, few-shot, chain-of-thought, critique, and expert) in adapting LLMs to code summarization tasks. Contrary to expectations, advanced prompting techniques may not outperform simple zero-shot prompting. Next, we investigate the impact of LLMs' model settings (including top\_p and temperature parameters) on the quality of generated summaries. We find the impact of the two parameters on summary quality varies by the base LLM and programming language, but their impacts are similar. Moreover, we canvass LLMs' abilities to summarize code snippets in distinct types of programming languages. The results reveal that LLMs perform suboptimally when summarizing code written in logic programming languages compared to other language types. Finally, we unexpectedly find that CodeLlama-Instruct with 7B parameters can outperform advanced GPT-4 in generating summaries describing code implementation details and asserting code properties. We hope that our findings can provide a comprehensive understanding of code summarization in the era of LLMs.