From Requirements to Architecture: An AI-Based Journey to Semi-Automatically Generate Software Architectures

• URL: http://arxiv.org/abs/2401.14079v1
• Date: Thu, 25 Jan 2024 10:56:58 GMT
• Title: From Requirements to Architecture: An AI-Based Journey to Semi-Automatically Generate Software Architectures
• Authors: Tobias Eisenreich, Sandro Speth, Stefan Wagner
• Abstract summary: 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.
• Score: 2.4150871564195007
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Designing domain models and software architectures represents a significant challenge in software development, as the resulting architectures play a vital role in fulfilling the system's quality of service. Due to time pressure, architects often model only one architecture based on their known limited domain understanding, patterns, and experience instead of thoroughly analyzing the domain and evaluating multiple candidates, selecting the best fitting. Existing approaches try to generate domain models based on requirements, but still require time-consuming manual effort to achieve good results. Therefore, in this vision paper, we propose a method to generate software architecture candidates semi-automatically based on requirements using artificial intelligence techniques. We further envision an automatic evaluation and trade-off analysis of the generated architecture candidates using, e.g., the architecture trade-off analysis method combined with large language models and quantitative analyses. To evaluate this approach, we aim to analyze the quality of the generated architecture models and the efficiency and effectiveness of our proposed process by conducting qualitative studies.

Related papers

• A Survey of Model Architectures in Information Retrieval [64.75808744228067]
  We focus on two key aspects: backbone models for feature extraction and end-to-end system architectures for relevance estimation. We trace the development from traditional term-based methods to modern neural approaches, particularly highlighting the impact of transformer-based models and subsequent large language models (LLMs) We conclude by discussing emerging challenges and future directions, including architectural optimizations for performance and scalability, handling of multimodal, multilingual data, and adaptation to novel application domains beyond traditional search paradigms.
  arXiv  Detail & Related papers  (2025-02-20T18:42:58Z)
• A quantitative framework for evaluating architectural patterns in ML systems [49.1574468325115]
  This study proposes a framework for quantitative assessment of architectural patterns in ML systems. We focus on scalability and performance metrics for cost-effective CPU-based inference.
  arXiv  Detail & Related papers  (2025-01-20T15:30:09Z)
• A Systematic Mapping Study on Architectural Approaches to Software Performance Analysis [8.629569588488328]
  This paper presents a systematic mapping study of 109 papers that integrate software architecture and performance analysis. We focus on five research questions that provide guidance for researchers and practitioners to gain an in-depth understanding of this research area.
  arXiv  Detail & Related papers  (2024-10-22T19:12:03Z)
• A Taxonomy of Architecture Options for Foundation Model-based Agents: Analysis and Decision Model [25.78239568393706]
  This paper introduces a taxonomy focused on the architectures of foundation-model-based agents. By unifying and detailing these classifications, our taxonomy aims to improve the design of foundation-model-based agents.
  arXiv  Detail & Related papers  (2024-08-06T03:10:52Z)
• Mechanistic Design and Scaling of Hybrid Architectures [114.3129802943915]
  We identify and test new hybrid architectures constructed from a variety of computational primitives. We experimentally validate the resulting architectures via an extensive compute-optimal and a new state-optimal scaling law analysis. We find MAD synthetics to correlate with compute-optimal perplexity, enabling accurate evaluation of new architectures.
  arXiv  Detail & Related papers  (2024-03-26T16:33:12Z)
• Surrogate-assisted Multi-objective Neural Architecture Search for Real-time Semantic Segmentation [11.866947846619064]
  neural architecture search (NAS) has emerged as a promising avenue toward automating the design of architectures. We propose a surrogate-assisted multi-objective method to address the challenges of applying NAS to semantic segmentation. Our method can identify architectures significantly outperforming existing state-of-the-art architectures designed both manually by human experts and automatically by other NAS methods.
  arXiv  Detail & Related papers  (2022-08-14T10:18:51Z)
• Computer vision-based analysis of buildings and built environments: A systematic review of current approaches [0.98314893665023]
  This paper presents a first systematic review of the computer vision-based analysis of buildings and the built environments. It reveals current research gaps and trends, and highlights two main categories of research aims.
  arXiv  Detail & Related papers  (2022-08-01T14:17:51Z)
• Image Quality Assessment in the Modern Age [53.19271326110551]
  This tutorial provides the audience with the basic theories, methodologies, and current progresses of image quality assessment (IQA) We will first revisit several subjective quality assessment methodologies, with emphasis on how to properly select visual stimuli. Both hand-engineered and (deep) learning-based methods will be covered.
  arXiv  Detail & Related papers  (2021-10-19T02:38:46Z)
• Dynamically Grown Generative Adversarial Networks [111.43128389995341]
  We propose a method to dynamically grow a GAN during training, optimizing the network architecture and its parameters together with automation. The method embeds architecture search techniques as an interleaving step with gradient-based training to periodically seek the optimal architecture-growing strategy for the generator and discriminator.
  arXiv  Detail & Related papers  (2021-06-16T01:25:51Z)
• Pareto-Frontier-aware Neural Architecture Generation for Diverse Budgets [93.79297053429447]
  Existing methods often perform an independent architecture search for each target budget. We propose a general architecture generator that automatically produces effective architectures for an arbitrary budget merely via model inference. Extensive experiments on three platforms (i.e., mobile, CPU, and GPU) show the superiority of the proposed method over existing NAS methods.
  arXiv  Detail & Related papers  (2021-02-27T13:59:17Z)

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.