From LLMs to LLM-based Agents for Software Engineering: A Survey of Current, Challenges and Future

• URL: http://arxiv.org/abs/2408.02479v1
• Date: Mon, 5 Aug 2024 14:01:15 GMT
• Title: From LLMs to LLM-based Agents for Software Engineering: A Survey of Current, Challenges and Future
• Authors: Haolin Jin, Linghan Huang, Haipeng Cai, Jun Yan, Bo Li, Huaming Chen,
• Abstract summary: We investigate the current practice and solutions for large language models (LLMs) and LLM-based agents for software engineering. In particular we summarise six key topics: requirement engineering, code generation, autonomous decision-making, software design, test generation, and software maintenance. We discuss the models and benchmarks used, providing a comprehensive analysis of their applications and effectiveness in software engineering.
• Score: 15.568939568441317
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the rise of large language models (LLMs), researchers are increasingly exploring their applications in var ious vertical domains, such as software engineering. LLMs have achieved remarkable success in areas including code generation and vulnerability detection. However, they also exhibit numerous limitations and shortcomings. LLM-based agents, a novel tech nology with the potential for Artificial General Intelligence (AGI), combine LLMs as the core for decision-making and action-taking, addressing some of the inherent limitations of LLMs such as lack of autonomy and self-improvement. Despite numerous studies and surveys exploring the possibility of using LLMs in software engineering, it lacks a clear distinction between LLMs and LLM based agents. It is still in its early stage for a unified standard and benchmarking to qualify an LLM solution as an LLM-based agent in its domain. In this survey, we broadly investigate the current practice and solutions for LLMs and LLM-based agents for software engineering. In particular we summarise six key topics: requirement engineering, code generation, autonomous decision-making, software design, test generation, and software maintenance. We review and differentiate the work of LLMs and LLM-based agents from these six topics, examining their differences and similarities in tasks, benchmarks, and evaluation metrics. Finally, we discuss the models and benchmarks used, providing a comprehensive analysis of their applications and effectiveness in software engineering. We anticipate this work will shed some lights on pushing the boundaries of LLM-based agents in software engineering for future research.

Related papers

• Large Language Model-Based Agents for Software Engineering: A Survey [20.258244647363544]
  The recent advance in Large Language Models (LLMs) has shaped a new paradigm of AI agents, i.e., LLM-based agents. We collect 106 papers and categorize them from two perspectives, i.e., the SE and agent perspectives. In addition, we discuss open challenges and future directions in this critical domain.
  arXiv  Detail & Related papers  (2024-09-04T15:59:41Z)
• Q*: Improving Multi-step Reasoning for LLMs with Deliberative Planning [53.6472920229013]
  Large Language Models (LLMs) have demonstrated impressive capability in many natural language tasks. LLMs are prone to produce errors, hallucinations and inconsistent statements when performing multi-step reasoning. We introduce Q*, a framework for guiding LLMs decoding process with deliberative planning.
  arXiv  Detail & Related papers  (2024-06-20T13:08:09Z)
• Large Language Models as Software Components: A Taxonomy for LLM-Integrated Applications [0.0]
  Large Language Models (LLMs) have become widely adopted recently. Research explores their use both as autonomous agents and as tools for software engineering. LLMs-integrated applications, on the other hand, are software systems that leverage an LLM to perform tasks that would otherwise be impossible or require significant coding effort. This study provides a taxonomy for LLM-integrated applications, offering a framework for analyzing and describing these systems.
  arXiv  Detail & Related papers  (2024-06-13T21:32:56Z)
• Small Models, Big Insights: Leveraging Slim Proxy Models To Decide When and What to Retrieve for LLMs [60.40396361115776]
  This paper introduces a novel collaborative approach, namely SlimPLM, that detects missing knowledge in large language models (LLMs) with a slim proxy model. We employ a proxy model which has far fewer parameters, and take its answers as answers. Heuristic answers are then utilized to predict the knowledge required to answer the user question, as well as the known and unknown knowledge within the LLM.
  arXiv  Detail & Related papers  (2024-02-19T11:11:08Z)
• Large Language Models: A Survey [69.72787936480394]
  Large Language Models (LLMs) have drawn a lot of attention due to their strong performance on a wide range of natural language tasks. LLMs' ability of general-purpose language understanding and generation is acquired by training billions of model's parameters on massive amounts of text data.
  arXiv  Detail & Related papers  (2024-02-09T05:37:09Z)
• Rethinking Interpretability in the Era of Large Language Models [76.1947554386879]
  Large language models (LLMs) have demonstrated remarkable capabilities across a wide array of tasks. The capability to explain in natural language allows LLMs to expand the scale and complexity of patterns that can be given to a human. These new capabilities raise new challenges, such as hallucinated explanations and immense computational costs.
  arXiv  Detail & Related papers  (2024-01-30T17:38:54Z)
• Knowledge Fusion of Large Language Models [73.28202188100646]
  This paper introduces the notion of knowledge fusion for large language models (LLMs) We externalize their collective knowledge and unique strengths, thereby elevating the capabilities of the target model beyond those of any individual source LLM. Our findings confirm that the fusion of LLMs can improve the performance of the target model across a range of capabilities such as reasoning, commonsense, and code generation.
  arXiv  Detail & Related papers  (2024-01-19T05:02:46Z)
• A Survey of Large Language Models for Code: Evolution, Benchmarking, and Future Trends [30.774685501251817]
  General large language models (LLMs) have demonstrated significant potential in tasks such as code generation in software engineering. A considerable portion of Code LLMs is derived from general LLMs through model fine-tuning. There is currently a lack of systematic investigation into Code LLMs and their performance.
  arXiv  Detail & Related papers  (2023-11-17T07:55:16Z)
• Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity [61.54815512469125]
  This survey addresses the crucial issue of factuality in Large Language Models (LLMs) As LLMs find applications across diverse domains, the reliability and accuracy of their outputs become vital.
  arXiv  Detail & Related papers  (2023-10-11T14:18:03Z)
• Towards an Understanding of Large Language Models in Software Engineering Tasks [29.30433406449331]
  Large Language Models (LLMs) have drawn widespread attention and research due to their astounding performance in text generation and reasoning tasks. The evaluation and optimization of LLMs in software engineering tasks, such as code generation, have become a research focus. This paper comprehensively investigate and collate the research and products combining LLMs with software engineering.
  arXiv  Detail & Related papers  (2023-08-22T12:37:29Z)
• Software Testing with Large Language Models: Survey, Landscape, and Vision [32.34617250991638]
  Pre-trained large language models (LLMs) have emerged as a breakthrough technology in natural language processing and artificial intelligence. This paper provides a comprehensive review of the utilization of LLMs in software testing.
  arXiv  Detail & Related papers  (2023-07-14T08:26:12Z)

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.