ALMAS: an Autonomous LLM-based Multi-Agent Software Engineering Framework
- URL: http://arxiv.org/abs/2510.03463v1
- Date: Fri, 03 Oct 2025 19:35:23 GMT
- Title: ALMAS: an Autonomous LLM-based Multi-Agent Software Engineering Framework
- Authors: Vali Tawosi, Keshav Ramani, Salwa Alamir, Xiaomo Liu,
- Abstract summary: We propose a vision for ALMAS, an Autonomous LLM-based Multi-Agent Software Engineering framework.<n>We showcase the progress towards ALMAS through our published works and a use case demonstrating the framework.
- Score: 5.920182474293131
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Multi-agent Large Language Model (LLM) systems have been leading the way in applied LLM research across a number of fields. One notable area is software development, where researchers have advanced the automation of code implementation, code testing, code maintenance, inter alia, using LLM agents. However, software development is a multifaceted environment that extends beyond just code. As such, a successful LLM system must factor in multiple stages of the software development life-cycle (SDLC). In this paper, we propose a vision for ALMAS, an Autonomous LLM-based Multi-Agent Software Engineering framework, which follows the above SDLC philosophy such that it may work within an agile software development team to perform several tasks end-to-end. ALMAS aligns its agents with agile roles, and can be used in a modular fashion to seamlessly integrate with human developers and their development environment. We showcase the progress towards ALMAS through our published works and a use case demonstrating the framework, where ALMAS is able to seamlessly generate an application and add a new feature.
Related papers
- Rethinking Technology Stack Selection with AI Coding Proficiency [49.617080246389605]
Large language models (LLMs) are now an integral part of software development.<n>We propose the concept, AI coding proficiency, the degree to which LLMs can utilize a given technology to generate high-quality code snippets.<n>We conduct the first comprehensive empirical study examining AI proficiency across 170 third-party libraries and 61 task scenarios.
arXiv Detail & Related papers (2025-09-14T06:56:47Z) - A Survey on Code Generation with LLM-based Agents [61.474191493322415]
Code generation agents powered by large language models (LLMs) are revolutionizing the software development paradigm.<n>LLMs are characterized by three core features.<n>This paper presents a systematic survey of the field of LLM-based code generation agents.
arXiv Detail & Related papers (2025-07-31T18:17:36Z) - LLMs' Reshaping of People, Processes, Products, and Society in Software Development: A Comprehensive Exploration with Early Adopters [3.4069804433026314]
Large language models (LLMs) like OpenAI ChatGPT, Google Gemini, and GitHub Copilot are rapidly gaining traction in the software industry.<n>Our study provides a nuanced understanding of how LLMs are shaping the landscape of software development.
arXiv Detail & Related papers (2025-03-06T22:27:05Z) - LLM-based Multi-Agent Systems: Techniques and Business Perspectives [26.74974842247119]
In the era of (multi-modal) large language models, most operational processes can be reformulated and reproduced using LLM agents.<n>As a natural trend of development, the tools for calling are becoming autonomous agents, thus the full intelligent system turns out to be a LLM-based Multi-Agent System (LaMAS)<n>Compared to the previous single-LLM-agent system, LaMAS has the advantages of i) dynamic task decomposition and organic specialization, ii) higher flexibility for system changing, and iv) feasibility of monetization for each entity.
arXiv Detail & Related papers (2024-11-21T11:36:29Z) - Human-In-the-Loop Software Development Agents [12.830816751625829]
Large Language Models (LLMs)-based multi-agent paradigms for software engineering are introduced to automatically resolve software development tasks.<n>In this paper, we introduce a Human-in-the-loop LLM-based Agents framework (HULA) for software development.<n>We design, implement, and deploy the HULA framework into Atlassian for internal uses.
arXiv Detail & Related papers (2024-11-19T23:22:33Z) - Multi-Programming Language Sandbox for LLMs [78.99934332554963]
out-of-the-box multi-programming language sandbox designed to provide unified and comprehensive feedback from compiler and analysis tools for Large Language Models (LLMs)
It can automatically identify the programming language of the code, compiling and executing it within an isolated sub-sandbox to ensure safety and stability.
arXiv Detail & Related papers (2024-10-30T14:46:43Z) - 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.<n>In particular we summarise six key topics: requirement engineering, code generation, autonomous decision-making, software design, test generation, and software maintenance.<n>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) - From Language Models to Practical Self-Improving Computer Agents [0.8547032097715571]
We develop a methodology to create AI computer agents that can carry out diverse computer tasks and self-improve.
We prompt an LLM agent to augment itself with retrieval, internet search, web navigation, and text editor capabilities.
The agent effectively uses these various tools to solve problems including automated software development and web-based tasks.
arXiv Detail & Related papers (2024-04-18T07:50:10Z) - If LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code
Empowers Large Language Models to Serve as Intelligent Agents [81.60906807941188]
Large language models (LLMs) are trained on a combination of natural language and formal language (code)
Code translates high-level goals into executable steps, featuring standard syntax, logical consistency, abstraction, and modularity.
arXiv Detail & Related papers (2024-01-01T16:51:20Z) - Experiential Co-Learning of Software-Developing Agents [83.34027623428096]
Large language models (LLMs) have brought significant changes to various domains, especially in software development.
We introduce Experiential Co-Learning, a novel LLM-agent learning framework.
Experiments demonstrate that the framework enables agents to tackle unseen software-developing tasks more effectively.
arXiv Detail & Related papers (2023-12-28T13:50:42Z) - AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation [61.455159391215915]
AutoGen is an open-source framework that allows developers to build LLM applications via multiple agents that can converse with each other to accomplish tasks.
AutoGen agents are customizable, conversable, and can operate in various modes that employ combinations of LLMs, human inputs, and tools.
arXiv Detail & Related papers (2023-08-16T05:57:52Z) - AgentBench: Evaluating LLMs as Agents [99.12825098528212]
Large Language Model (LLM) as agents has been widely acknowledged recently.<n>We present AgentBench, a benchmark that consists of 8 distinct environments to assess LLM-as-Agent's reasoning and decision-making abilities.
arXiv Detail & Related papers (2023-08-07T16:08:11Z)
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.