This repository collects papers that utilize Large Language Models (LLMs) for Software Engineering (SE) tasks. The catalog information is derived from our research paper, Large Language Models for Software Engineering: A Systematic Literature Review
Key Points
LLMs have significantly impacted numerous domains, including SE. Many recent publications have explored the application of LLMs to various SE tasks. However, a comprehensive understanding of the application, effects, and possible limitations of LLMs on SE is still in its early stages.
To bridge this gap, we conducted a systematic literature review on LLM4SE, focusing particularly on understanding how LLMs can be exploited to optimize processes and outcomes. We collected and analyzed 229 research papers from 2017 to 2023 to answer four key research questions (RQs):
- RQ1: We categorize different LLMs that have been employed in SE tasks, characterizing their distinctive features and uses.
- RQ2: We analyze the methods used in data collection, preprocessing, and application, highlighting the role of well-curated datasets for successful LLM for SE implementation.
- RQ3: We investigate the strategies employed to optimize and evaluate the performance of LLMs in SE.
- RQ4: We examine the specific SE tasks where LLMs have shown success to date, illustrating their practical contributions to the field.
From the answers to these RQs, we discuss the current state-of-the-art and trends, identify gaps in existing research, and flag promising areas for future study.