Generative Software Engineering

• URL: http://arxiv.org/abs/2403.02583v2
• Date: Wed, 3 Apr 2024 13:55:01 GMT
• Title: Generative Software Engineering
• Authors: Yuan Huang, Yinan Chen, Xiangping Chen, Junqi Chen, Rui Peng, Zhicao Tang, Jinbo Huang, Furen Xu, Zibin Zheng,
• Abstract summary: We present a literature review of generative tasks in Software engineering using pre-trained models and large language models (LLMs) LLMs possess powerful language representation and contextual awareness capabilities, enabling them to leverage diverse training data and adapt to generative tasks. We identify key strengths, weaknesses, and gaps in existing approaches, and propose potential research directions.
• Score: 23.584814591463406
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rapid development of deep learning techniques, improved computational power, and the availability of vast training data have led to significant advancements in pre-trained models and large language models (LLMs). Pre-trained models based on architectures such as BERT and Transformer, as well as LLMs like ChatGPT, have demonstrated remarkable language capabilities and found applications in Software engineering. Software engineering tasks can be divided into many categories, among which generative tasks are the most concern by researchers, where pre-trained models and LLMs possess powerful language representation and contextual awareness capabilities, enabling them to leverage diverse training data and adapt to generative tasks through fine-tuning, transfer learning, and prompt engineering. These advantages make them effective tools in generative tasks and have demonstrated excellent performance. In this paper, we present a comprehensive literature review of generative tasks in SE using pre-trained models and LLMs. We accurately categorize SE generative tasks based on software engineering methodologies and summarize the advanced pre-trained models and LLMs involved, as well as the datasets and evaluation metrics used. Additionally, we identify key strengths, weaknesses, and gaps in existing approaches, and propose potential research directions. This review aims to provide researchers and practitioners with an in-depth analysis and guidance on the application of pre-trained models and LLMs in generative tasks within SE.

Related papers

• MLGym: A New Framework and Benchmark for Advancing AI Research Agents [51.9387884953294]
  We introduce Meta MLGym and MLGym-Bench, a new framework and benchmark for evaluating and developing large language models on AI research tasks. This is the first Gym environment for machine learning (ML) tasks, enabling research on reinforcement learning (RL) algorithms for training such agents. We evaluate a number of frontier large language models (LLMs) on our benchmarks such as Claude-3.5-Sonnet, Llama-3.1 405B, GPT-4o, o1-preview, and Gemini-1.5 Pro.
  arXiv  Detail & Related papers  (2025-02-20T12:28:23Z)
• From Selection to Generation: A Survey of LLM-based Active Learning [153.8110509961261]
  Large Language Models (LLMs) have been employed for generating entirely new data instances and providing more cost-effective annotations. This survey aims to serve as an up-to-date resource for researchers and practitioners seeking to gain an intuitive understanding of LLM-based AL techniques.
  arXiv  Detail & Related papers  (2025-02-17T12:58:17Z)
• Personalized Multimodal Large Language Models: A Survey [127.9521218125761]
  Multimodal Large Language Models (MLLMs) have become increasingly important due to their state-of-the-art performance and ability to integrate multiple data modalities. This paper presents a comprehensive survey on personalized multimodal large language models, focusing on their architecture, training methods, and applications.
  arXiv  Detail & Related papers  (2024-12-03T03:59:03Z)
• Do Advanced Language Models Eliminate the Need for Prompt Engineering in Software Engineering? [18.726229967976316]
  This paper reevaluates various prompt engineering techniques within the context of advanced Large Language Models (LLMs) Our findings reveal that prompt engineering techniques developed for earlier LLMs may provide diminished benefits or even hinder performance when applied to advanced models.
  arXiv  Detail & Related papers  (2024-11-04T13:56:37Z)
• A Survey of Small Language Models [104.80308007044634]
  Small Language Models (SLMs) have become increasingly important due to their efficiency and performance to perform various language tasks with minimal computational resources. We present a comprehensive survey on SLMs, focusing on their architectures, training techniques, and model compression techniques.
  arXiv  Detail & Related papers  (2024-10-25T23:52:28Z)
• Forewarned is Forearmed: Leveraging LLMs for Data Synthesis through Failure-Inducing Exploration [90.41908331897639]
  Large language models (LLMs) have significantly benefited from training on diverse, high-quality task-specific data. We present a novel approach, ReverseGen, designed to automatically generate effective training samples.
  arXiv  Detail & Related papers  (2024-10-22T06:43:28Z)
• Large Language Models in Computer Science Education: A Systematic Literature Review [7.240148550817106]
  Large language models (LLMs) are becoming increasingly better at a wide range of Natural Language Processing tasks (NLP) Recently, these models have extended their capabilities to coding tasks, bridging the gap between natural languages (NL) and programming languages (PL)
  arXiv  Detail & Related papers  (2024-10-21T17:49:50Z)
• 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)
• Aligning Large Language Models with Human: A Survey [53.6014921995006]
  Large Language Models (LLMs) trained on extensive textual corpora have emerged as leading solutions for a broad array of Natural Language Processing (NLP) tasks. Despite their notable performance, these models are prone to certain limitations such as misunderstanding human instructions, generating potentially biased content, or factually incorrect information. This survey presents a comprehensive overview of these alignment technologies, including the following aspects.
  arXiv  Detail & Related papers  (2023-07-24T17:44:58Z)
• 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)
• How Effective are Large Language Models in Generating Software Specifications? [14.170320751508502]
  Large Language Models (LLMs) have been successfully applied to numerous Software Engineering (SE) tasks. We conduct the first empirical study to evaluate the capabilities of LLMs for generating software specifications from software comments or documentation.
  arXiv  Detail & Related papers  (2023-06-06T00:28:39Z)

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.