Domain-Driven Design in Software Development: A Systematic Literature Review on Implementation, Challenges, and Effectiveness

• URL: http://arxiv.org/abs/2310.01905v3
• Date: Thu, 9 Nov 2023 10:58:46 GMT
• Title: Domain-Driven Design in Software Development: A Systematic Literature Review on Implementation, Challenges, and Effectiveness
• Authors: Ozan \"Ozkan, \"Onder Babur, Mark van den Brand
• Abstract summary: Domain-Driven Design (DDD) addresses software challenges, gaining attention for academia, reimplementation, and adoption. This Systematic Literature Review (SLR) analyzes DDD research in software development to assess its effectiveness in solving architecture problems.
• Score: 0.18726646412385334
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Context: Domain-Driven Design (DDD) addresses software challenges, gaining attention for refactoring, reimplementation, and adoption. It centers on domain knowledge to solve complex business problems. Objective: This Systematic Literature Review (SLR) analyzes DDD research in software development to assess its effectiveness in solving architecture problems, identify challenges, and explore outcomes. Method: We selected 36 peer-reviewed studies and conducted quantitative and qualitative analysis. Results: DDD effectively improved software systems, emphasizing Ubiquitous Language, Bounded Context, and Domain Events. DDD in microservices gained prominence for system decomposition. Some studies lacked empirical evaluations, identifying challenges in onboarding and expertise. Conclusion: Adopting DDD benefits software development, involving stakeholders like engineers, architects, managers, and domain experts. More empirical evaluations and open discussions on challenges are needed. Collaboration between academia and industry advances DDD adoption and knowledge transfer in projects.

Related papers

• Who is to Blame: A Comprehensive Review of Challenges and Opportunities in Designer-Developer Collaboration [20.90113991224472]
  Software development relies on effective collaboration between Software Development Engineers (SDEs) and User eXperience Designers (UXDs) to create software products of high quality and usability. We conducted a systematic literature review of 45 papers published since 2004 to uncover three key collaboration challenges and two main categories of potential best practices. Our findings have broad applicability for collaboration in software development, extending beyond the partnership between SDEs and UXDs.
  arXiv  Detail & Related papers  (2025-01-20T21:11:06Z)
• SoK: On Closing the Applicability Gap in Automated Vulnerability Detection [0.18846515534317265]
  Automated Vulnerability Detection (AVD) aims to autonomously analyze source code to identify vulnerabilities. This paper addresses two primary research questions: How is current AVD research distributed across its core components, and what key areas should future research target to bridge the gap in the practical applicability of AVD throughout software development? We conduct a systematization over 79 AVD articles and 17 empirical studies, analyzing them across five core components: task formulation and granularity, input programming languages and representations, detection approaches and key solutions, evaluation metrics and datasets, and reported performance.
  arXiv  Detail & Related papers  (2024-12-15T14:01:41Z)
• A Comparative Study on the Impact of Test-Driven Development (TDD) and Behavior-Driven Development (BDD) on Enterprise Software Delivery Effectiveness [0.4532517021515834]
  This paper compares the impact of Test-Driven Development (TDD) and Behavior-Driven Development (BDD) on software delivery effectiveness within enterprise environments. The findings reveal distinct effects of each model on delivery speed, software quality, and team collaboration.
  arXiv  Detail & Related papers  (2024-11-05T06:47:11Z)
• Evaluation of OpenAI o1: Opportunities and Challenges of AGI [112.0812059747033]
  o1-preview demonstrated remarkable capabilities, often achieving human-level or superior performance. The model excelled in tasks requiring intricate reasoning and knowledge integration across various fields. Overall results indicate significant progress towards artificial general intelligence.
  arXiv  Detail & Related papers  (2024-09-27T06:57:00Z)
• Diagnostic Reasoning in Natural Language: Computational Model and Application [68.47402386668846]
  We investigate diagnostic abductive reasoning (DAR) in the context of language-grounded tasks (NL-DAR) We propose a novel modeling framework for NL-DAR based on Pearl's structural causal models. We use the resulting dataset to investigate the human decision-making process in NL-DAR.
  arXiv  Detail & Related papers  (2024-09-09T06:55:37Z)
• A Comprehensive Survey on Underwater Image Enhancement Based on Deep Learning [51.7818820745221]
  Underwater image enhancement (UIE) presents a significant challenge within computer vision research. Despite the development of numerous UIE algorithms, a thorough and systematic review is still absent.
  arXiv  Detail & Related papers  (2024-05-30T04:46:40Z)
• Exploring Data Management Challenges and Solutions in Agile Software Development: A Literature Review and Practitioner Survey [4.45543024542181]
  Managing data related to a software product and its development poses significant challenges for software projects and agile development teams. These include integrating data from diverse sources and ensuring data quality amidst continuous change and adaptation. The paper systematically explores data management challenges and potential solutions in agile projects.
  arXiv  Detail & Related papers  (2024-02-01T10:07:12Z)
• Overview of Robust and Multilingual Automatic Evaluation Metrics for Open-Domain Dialogue Systems at DSTC 11 Track 4 [51.142614461563184]
  This track in the 11th Dialogue System Technology Challenge (DSTC11) is part of the ongoing effort to promote robust and multilingual automatic evaluation metrics. This article describes the datasets and baselines provided to participants and discusses the submission and result details of the two proposed subtasks.
  arXiv  Detail & Related papers  (2023-06-22T10:50:23Z)
• AI for Agile development: a Meta-Analysis [0.0]
  This study explores the benefits and challenges of integrating Artificial Intelligence with Agile software development methodologies. The review helped identify critical challenges, such as the need for specialised socio-technical expertise. Further research is needed to better understand its impact on processes and practitioners, and to address the indirect challenges associated with its implementation.
  arXiv  Detail & Related papers  (2023-05-14T08:10:40Z)
• Artificial Intelligence for IT Operations (AIOPS) Workshop White Paper [50.25428141435537]
  Artificial Intelligence for IT Operations (AIOps) is an emerging interdisciplinary field arising in the intersection between machine learning, big data, streaming analytics, and the management of IT operations. Main aim of the AIOPS workshop is to bring together researchers from both academia and industry to present their experiences, results, and work in progress in this field.
  arXiv  Detail & Related papers  (2021-01-15T10:43:10Z)
• Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities [52.59080024266596]
  We present a survey of the state-of-the-art deep learning methods for sensor-based human activity recognition. We first introduce the multi-modality of the sensory data and provide information for public datasets. We then propose a new taxonomy to structure the deep methods by challenges.
  arXiv  Detail & Related papers  (2020-01-21T09:55:59Z)

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.