Towards a Definition of Complex Software System
- URL: http://arxiv.org/abs/2306.11817v1
- Date: Tue, 20 Jun 2023 18:22:21 GMT
- Title: Towards a Definition of Complex Software System
- Authors: Jan \v{Z}i\v{z}ka, Bruno Rossi, Tom\'a\v{s} Pitner
- Abstract summary: We adopt the theory-to-research strategy to extract properties of Complex Systems from research in other fields to create a formal definition of a Complex Software System.
Overall, the definition will allow for a more precise, consistent, and rigorous frame of reference for conducting scientific research on software systems.
- Score: 0.25782420501870285
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Complex Systems were identified and studied in different fields, such as
physics, biology, and economics. These systems exhibit exciting properties such
as self-organization, robust order, and emergence. In recent years, software
systems displaying behaviors associated with Complex Systems are starting to
appear, and these behaviors are showing previously unknown potential (e.g.,
GPT-based applications). Yet, there is no commonly shared definition of a
Complex Software System that can serve as a key reference for academia to
support research in the area. In this paper, we adopt the theory-to-research
strategy to extract properties of Complex Systems from research in other
fields, mapping them to software systems to create a formal definition of a
Complex Software System. We support the evolution of the properties through
future validation, and we provide examples of the application of the
definition. Overall, the definition will allow for a more precise, consistent,
and rigorous frame of reference for conducting scientific research on software
systems.
Related papers
- From Web Search towards Agentic Deep Research: Incentivizing Search with Reasoning Agents [96.65646344634524]
Large Language Models (LLMs), endowed with reasoning and agentic capabilities, are ushering in a new paradigm termed Agentic Deep Research.<n>We trace the evolution from static web search to interactive, agent-based systems that plan, explore, and learn.<n>We demonstrate that Agentic Deep Research not only significantly outperforms existing approaches, but is also poised to become the dominant paradigm for future information seeking.
arXiv Detail & Related papers (2025-06-23T17:27:19Z) - A Comprehensive Survey of Deep Research: Systems, Methodologies, and Applications [3.002468101812191]
We analyze more than 80 commercial and non-commercial implementations that have emerged since 2023.<n>We propose a novel hierarchical taxonomy that categorizes systems according to four fundamental technical dimensions.<n>Our analysis reveals both the significant capabilities of current implementations and the technical and ethical challenges they present.
arXiv Detail & Related papers (2025-06-14T18:19:05Z) - Statistical complexity of software systems represented as multi-layer networks [0.0]
We propose the adoption of statistical complexity as an empirical measure for evaluating the complexity of software systems.
Our approach involves calculating the statistical complexity of software systems modeled as multi-layer networks validated by simulations and theoretical comparisons.
This measure offers insights into the organizational structure of software systems, exhibits promising consistency with theoretical expectations, and paves the way for leveraging statistical complexity as a tool to deepen our understanding of complex software systems.
arXiv Detail & Related papers (2025-03-29T12:33:52Z) - Large Language Model Agent: A Survey on Methodology, Applications and Challenges [88.3032929492409]
Large Language Model (LLM) agents, with goal-driven behaviors and dynamic adaptation capabilities, potentially represent a critical pathway toward artificial general intelligence.
This survey systematically deconstructs LLM agent systems through a methodology-centered taxonomy.
Our work provides a unified architectural perspective, examining how agents are constructed, how they collaborate, and how they evolve over time.
arXiv Detail & Related papers (2025-03-27T12:50:17Z) - Towards the Structure and Mechanisms of Complex Systems, the Approach of the Quantitative Theory of Meaning [0.0]
We study analysis of complex systems using a Quantitative Theory of Meaning developed as an extention of Shannon's Communication Theory.
The dynamics of the system are provided by reflexive communication between heterogenious agents.
arXiv Detail & Related papers (2024-12-12T07:18:47Z) - Order-theoretic models for decision-making: Learning, optimization, complexity and computation [0.0]
The study of intelligent systems explains behaviour in terms of economic rationality.
The first aim of this thesis is to clarify the applicability of these results in the study of intelligent systems.
arXiv Detail & Related papers (2024-06-15T20:20:43Z) - Looking back and forward: A retrospective and future directions on Software Engineering for systems-of-systems [0.11470070927586014]
The textitInternational Workshop on Software Engineering for Systems-of-Systems (SESoS) series started in 2013 to fill a gap in scientific forums addressing SoS from the Software Engineering perspective.
This article presents a study aimed at outlining the evolution and future trajectory of Software Engineering for SoS based on the examination of 57 papers spanning the 11 editions of the SESoS workshop (2013-2023).
arXiv Detail & Related papers (2024-03-25T13:12:39Z) - A Systems-Theoretical Formalization of Closed Systems [47.99822253865054]
There is a lack of formalism for some key foundational concepts in systems engineering.
One of the most recently acknowledged deficits is the inadequacy of systems engineering practices for intelligent systems.
arXiv Detail & Related papers (2023-11-16T19:01:01Z) - Unsupervised Learning in Complex Systems [0.0]
This thesis explores the use of complex systems to study learning and adaptation in natural and artificial systems.
The goal is to develop autonomous systems that can learn without supervision, develop on their own, and become increasingly complex over time.
arXiv Detail & Related papers (2023-07-11T19:48:42Z) - A Graphical Modeling Language for Artificial Intelligence Applications
in Automation Systems [69.50862982117127]
An interdisciplinary graphical modeling language that enables the modeling of an AI application as an overall system comprehensible to all disciplines does not yet exist.
This paper presents a graphical modeling language that enables consistent and understandable modeling of AI applications in automation systems at system level.
arXiv Detail & Related papers (2023-06-20T12:06:41Z) - Quantifying Complexity: An Object-Relations Approach to Complex Systems [0.0]
This paper develops an object-relations model of complex systems which generalizes to systems of all types.
The resulting Complex Information Entropy (CIE) equation is a robust method to quantify complexity across various contexts.
Applications are discussed in the fields of engineering design, atomic and molecular physics, chemistry, materials science, psychology, neuroscience, sociology, ecology, economics, and medicine.
arXiv Detail & Related papers (2022-10-22T04:22:21Z) - Symmetry Group Equivariant Architectures for Physics [52.784926970374556]
In the domain of machine learning, an awareness of symmetries has driven impressive performance breakthroughs.
We argue that both the physics community and the broader machine learning community have much to understand.
arXiv Detail & Related papers (2022-03-11T18:27:04Z) - An Extensible Benchmark Suite for Learning to Simulate Physical Systems [60.249111272844374]
We introduce a set of benchmark problems to take a step towards unified benchmarks and evaluation protocols.
We propose four representative physical systems, as well as a collection of both widely used classical time-based and representative data-driven methods.
arXiv Detail & Related papers (2021-08-09T17:39:09Z) - An Overview of Recommender Systems and Machine Learning in Feature
Modeling and Configuration [55.67505546330206]
We give an overview of a potential new line of research which is related to the application of recommender systems and machine learning techniques.
In this paper, we give examples of the application of recommender systems and machine learning and discuss future research issues.
arXiv Detail & Related papers (2021-02-12T17:21:36Z) - The 4th International Workshop on Smart Simulation and Modelling for
Complex Systems [4.489415125484399]
Computer-based modelling and simulation have become useful tools to facilitate humans to understand systems in different domains.
Smart systems such as multi-agent systems have demonstrated advantages and great potentials in modelling and simulating complex systems.
arXiv Detail & Related papers (2021-02-01T21:40:28Z) - BUSTLE: Bottom-Up Program Synthesis Through Learning-Guided Exploration [72.88493072196094]
We present a new synthesis approach that leverages learning to guide a bottom-up search over programs.
In particular, we train a model to prioritize compositions of intermediate values during search conditioned on a set of input-output examples.
We show that the combination of learning and bottom-up search is remarkably effective, even with simple supervised learning approaches.
arXiv Detail & Related papers (2020-07-28T17:46:18Z)
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.