LLMs: A Game-Changer for Software Engineers?

• URL: http://arxiv.org/abs/2411.00932v1
• Date: Fri, 01 Nov 2024 17:14:37 GMT
• Title: LLMs: A Game-Changer for Software Engineers?
• Authors: Md Asraful Haque,
• Abstract summary: Large Language Models (LLMs) like GPT-3 and GPT-4 have emerged as groundbreaking innovations with capabilities that extend far beyond traditional AI applications. Their potential to revolutionize software development has captivated the software engineering (SE) community. This paper argues that LLMs are not just reshaping how software is developed but are redefining the role of developers.
• Score: 0.0
• License:
• Abstract: Large Language Models (LLMs) like GPT-3 and GPT-4 have emerged as groundbreaking innovations with capabilities that extend far beyond traditional AI applications. These sophisticated models, trained on massive datasets, can generate human-like text, respond to complex queries, and even write and interpret code. Their potential to revolutionize software development has captivated the software engineering (SE) community, sparking debates about their transformative impact. Through a critical analysis of technical strengths, limitations, real-world case studies, and future research directions, this paper argues that LLMs are not just reshaping how software is developed but are redefining the role of developers. While challenges persist, LLMs offer unprecedented opportunities for innovation and collaboration. Early adoption of LLMs in software engineering is crucial to stay competitive in this rapidly evolving landscape. This paper serves as a guide, helping developers, organizations, and researchers understand how to harness the power of LLMs to streamline workflows and acquire the necessary skills.

Related papers

• From LLMs to LLM-based Agents for Software Engineering: A Survey of Current, Challenges and Future [15.568939568441317]
  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.
  arXiv  Detail & Related papers  (2024-08-05T14:01:15Z)
• Agent-Driven Automatic Software Improvement [55.2480439325792]
  This research proposal aims to explore innovative solutions by focusing on the deployment of agents powered by Large Language Models (LLMs) The iterative nature of agents, which allows for continuous learning and adaptation, can help surpass common challenges in code generation. We aim to use the iterative feedback in these systems to further fine-tune the LLMs underlying the agents, becoming better aligned to the task of automated software improvement.
  arXiv  Detail & Related papers  (2024-06-24T15:45:22Z)
• A Survey on Self-Evolution of Large Language Models [116.54238664264928]
  Large language models (LLMs) have significantly advanced in various fields and intelligent agent applications. To address this issue, self-evolution approaches that enable LLMs to autonomously acquire, refine, and learn from experiences generated by the model itself are rapidly growing.
  arXiv  Detail & Related papers  (2024-04-22T17:43:23Z)
• An Empirical Study on Usage and Perceptions of LLMs in a Software Engineering Project [1.433758865948252]
  Large Language Models (LLMs) represent a leap in artificial intelligence, excelling in tasks using human language(s) In this paper, we analyze the AI-generated code, prompts used for code generation, and the human intervention levels to integrate the code into the code base. Our findings suggest that LLMs can play a crucial role in the early stages of software development.
  arXiv  Detail & Related papers  (2024-01-29T14:32:32Z)
• LLM4EDA: Emerging Progress in Large Language Models for Electronic Design Automation [74.7163199054881]
  Large Language Models (LLMs) have demonstrated their capability in context understanding, logic reasoning and answer generation. We present a systematic study on the application of LLMs in the EDA field. We highlight the future research direction, focusing on applying LLMs in logic synthesis, physical design, multi-modal feature extraction and alignment of circuits.
  arXiv  Detail & Related papers  (2023-12-28T15:09:14Z)
• Lessons from Building StackSpot AI: A Contextualized AI Coding Assistant [2.268415020650315]
  A new breed of tools, built atop Large Language Models, is emerging. These tools aim to mitigate drawbacks by employing techniques like fine-tuning or enriching user prompts with contextualized information.
  arXiv  Detail & Related papers  (2023-11-30T10:51:26Z)
• LMRL Gym: Benchmarks for Multi-Turn Reinforcement Learning with Language Models [56.25156596019168]
  This paper introduces the LMRL-Gym benchmark for evaluating multi-turn RL for large language models (LLMs) Our benchmark consists of 8 different language tasks, which require multiple rounds of language interaction and cover a range of tasks in open-ended dialogue and text games.
  arXiv  Detail & Related papers  (2023-11-30T03:59:31Z)
• Large Language Models for Software Engineering: Survey and Open Problems [35.29302720251483]
  This paper provides a survey of the emerging area of Large Language Models (LLMs) for Software Engineering (SE) Our survey reveals the pivotal role that hybrid techniques (traditional SE plus LLMs) have to play in the development and deployment of reliable, efficient and effective LLM-based SE.
  arXiv  Detail & Related papers  (2023-10-05T13:33:26Z)
• Federated Fine-Tuning of LLMs on the Very Edge: The Good, the Bad, the Ugly [62.473245910234304]
  This paper takes a hardware-centric approach to explore how Large Language Models can be brought to modern edge computing systems. We provide a micro-level hardware benchmark, compare the model FLOP utilization to a state-of-the-art data center GPU, and study the network utilization in realistic conditions.
  arXiv  Detail & Related papers  (2023-10-04T20:27:20Z)
• How Can Recommender Systems Benefit from Large Language Models: A Survey [82.06729592294322]
  Large language models (LLM) have shown impressive general intelligence and human-like capabilities. We conduct a comprehensive survey on this research direction from the perspective of the whole pipeline in real-world recommender systems.
  arXiv  Detail & Related papers  (2023-06-09T11:31:50Z)
• Automatically Generating CS Learning Materials with Large Language Models [4.526618922750769]
  Large Language Models (LLMs) enable software developers to generate code based on a natural language prompt. LLMs may enable students to interact with code in new ways while helping instructors scale their learning materials. LLMs also introduce new implications for academic integrity, curriculum design, and software engineering careers.
  arXiv  Detail & Related papers  (2022-12-09T20:37:44Z)

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.