From Requirements to Architecture: Semi-Automatically Generating Software Architectures

• URL: http://arxiv.org/abs/2504.12192v1
• Date: Wed, 16 Apr 2025 15:46:56 GMT
• Title: From Requirements to Architecture: Semi-Automatically Generating Software Architectures
• Authors: Tobias Eisenreich,
• Abstract summary: This method involves the architect's close collaboration with LLM-fueled tooling over the whole process.<n>The architect is guided through Domain Model creation, Use Case specification, architectural decisions, and architecture evaluation.<n>Preliminary results suggest the feasibility of this process and indicate major time savings for the architect.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: To support junior and senior architects, I propose developing a new architecture creation method that leverages LLMs' evolving capabilities to support the architect. This method involves the architect's close collaboration with LLM-fueled tooling over the whole process. The architect is guided through Domain Model creation, Use Case specification, architectural decisions, and architecture evaluation. While the architect can take complete control of the process and the results, and use the tooling as a building set, they can follow the intended process for maximum tooling support. The preliminary results suggest the feasibility of this process and indicate major time savings for the architect.

Related papers

• The Architecture Tradeoff and Risk Analysis Framework (ATRAF): A Unified Approach for Evaluating Software Architectures, Reference Architectures, and Architectural Frameworks [0.0]
  We introduce the Architecture Tradeoff and Risk Analysis Framework (ATRAF) ATRAF is a scenario-driven framework for evaluating tradeoffs and risks across architectural levels. It enables the identification of sensitivities, tradeoffs, and risks while supporting continuous refinement of architectural artifacts.
  arXiv  Detail & Related papers  (2025-05-01T17:48:52Z)
• ARLO: A Tailorable Approach for Transforming Natural Language Software Requirements into Architecture using LLMs [0.0]
  Software requirements expressed in natural language (NL) frequently suffer from verbosity, ambiguity, and inconsistency.<n>This paper proposes ARLO, an approach that automates the task of mapping NL requirements to architecture.
  arXiv  Detail & Related papers  (2025-04-08T15:38:42Z)
• Knowledge-Based Multi-Agent Framework for Automated Software Architecture Design [8.082263503892912]
  We envision a Knowledge-based Multi-Agent Architecture Design (MAAD) framework.<n>MAAD uses agents to simulate human roles in the traditional software architecture design process.<n>We aim to advance the full automation of application-level system development.
  arXiv  Detail & Related papers  (2025-03-26T13:35:10Z)
• Semi-Automated Design of Data-Intensive Architectures [49.1574468325115]
  This paper introduces a development methodology for data-intensive architectures.<n>It guides architects in (i) designing a suitable architecture for their specific application scenario, and (ii) selecting an appropriate set of concrete systems to implement the application.<n>We show that the description languages we adopt can capture the key aspects of data-intensive architectures proposed by researchers and practitioners.
  arXiv  Detail & Related papers  (2025-03-21T16:01:11Z)
• Building Optimal Neural Architectures using Interpretable Knowledge [15.66288233048004]
  AutoBuild is a scheme which learns to align the latent embeddings of operations and architecture modules with the ground-truth performance of the architectures they appear in. We show that by mining a relatively small set of evaluated architectures, AutoBuild can learn to build high-quality architectures directly or help to reduce search space to focus on relevant areas.
  arXiv  Detail & Related papers  (2024-03-20T04:18:38Z)
• From Requirements to Architecture: An AI-Based Journey to Semi-Automatically Generate Software Architectures [2.4150871564195007]
  We propose a method to generate software architecture candidates based on requirements using artificial intelligence techniques. We further envision an automatic evaluation and trade-off analysis of the generated architecture candidates.
  arXiv  Detail & Related papers  (2024-01-25T10:56:58Z)
• Serving Deep Learning Model in Relational Databases [70.53282490832189]
  Serving deep learning (DL) models on relational data has become a critical requirement across diverse commercial and scientific domains. We highlight three pivotal paradigms: The state-of-the-art DL-centric architecture offloads DL computations to dedicated DL frameworks. The potential UDF-centric architecture encapsulates one or more tensor computations into User Defined Functions (UDFs) within the relational database management system (RDBMS)
  arXiv  Detail & Related papers  (2023-10-07T06:01:35Z)
• Enhancing Architecture Frameworks by Including Modern Stakeholders and their Views/Viewpoints [48.87872564630711]
  The stakeholders with data science and Machine Learning related concerns, such as data scientists and data engineers, are yet to be included in existing architecture frameworks.<n>We surveyed 61 subject matter experts from over 25 organizations in 10 countries.
  arXiv  Detail & Related papers  (2023-08-09T21:54:34Z)
• Unfinished Architectures: A Perspective from Artificial Intelligence [73.52315464582637]
  Development of Artificial Intelligence (AI) opens new avenues for the proposal of possibilities for the completion of unfinished architectures. Recent appearance of tools such as DALL-E, capable of completing images guided by a textual description. In this article we explore the use of these new AI tools for the completion of unfinished facades of historical temples and analyse the still germinal stadium in the field of architectural graphic composition.
  arXiv  Detail & Related papers  (2023-03-03T13:05:10Z)
• Network Graph Based Neural Architecture Search [57.78724765340237]
  We search neural network by rewiring the corresponding graph and predict the architecture performance by graph properties. Because we do not perform machine learning over the entire graph space, the searching process is remarkably efficient.
  arXiv  Detail & Related papers  (2021-12-15T00:12:03Z)
• AnyDB: An Architecture-less DBMS for Any Workload [0.0]
  Instead of hard-baking an architectural model, such as a shared-nothing architecture, we aim for a new class of so-called architecture-lesss. Our initial results show that our architecture-less AnyDB can provide significant speed-ups across varying workloads.
  arXiv  Detail & Related papers  (2020-09-04T15:38:27Z)
• Stage-Wise Neural Architecture Search [65.03109178056937]
  Modern convolutional networks such as ResNet and NASNet have achieved state-of-the-art results in many computer vision applications. These networks consist of stages, which are sets of layers that operate on representations in the same resolution. It has been demonstrated that increasing the number of layers in each stage improves the prediction ability of the network. However, the resulting architecture becomes computationally expensive in terms of floating point operations, memory requirements and inference time.
  arXiv  Detail & Related papers  (2020-04-23T14:16:39Z)

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.