# Digital Twins in Software Engineering--A Systematic Literature Review and Vision
## 1 Introduction  
On 10 October 2016, the Fourth Industrial Revolution (4 IR) was popularized by the World Economic Forum [1] from the \"Industry 4.0\" concept that originated in 2011 at the Hannover Fair. The 4 IR introduces how a highly optimized production system capable of delivering extensive customization focused on customer satisfaction can be developed, while evaluating its potential impact on sustainability. A key characteristic of this revolution is the intensive use of technology in symbiosis with humans to complete the digital transformation toward smart factories. Complying with the principles and fundamentals defined by the Fourth Industrial Revolution involves the application and integration of multiple technologies such as the Internet of Things (IoT), big data, mixed reality, blockchain, 5G, machine learning, cloud computing, digital twins (DTIs), or cyber-physical systems (CPSs) [2, 3]. From these technologies, the DT emerged as a critical technology of Industry 4.0. Initially, a DT is defined as a virtual representation of the physical counterpart (device, machine, system), a digital model that mirrors the real-world counterpart in terms ofits characteristics, behavior, and conditions [4]. For this reason, they have been applied to many industries to enhance operations, predict needs, improve decision making, or optimize performance. The digital twin definition is still evolving in order to include a virtual representation, not only of the physical elements of a system but also its processes, models, or components to be present in the development, deployment, maintenance, and management in general of any product, as it is required in product lifecycle management (PLM). In the same way, the software industry must manage processes, models, components, and tools to make possible the development, deployment, maintenance, and management of software systems in the context of application lifecycle management (ALM). Therefore, there are similarities between PLM (focused on hardware) and application lifecycle management (focused on software). [PERSON] has already published articles analyzing the convergence of both concepts [5; 6]. However, the growing complexity of software engineering activities and their particularity requires setting a stable ground from the definition and identifying the relevant points to consider, setting the foundations for applying software development lifecycle management (SDLM, as a part of ALM) and ALM. This can be the reason why the impact of DTs in the software industry is still limited. Historically, the software industry has adopted many concepts from traditional industries to improve efficiency and productivity. ALM covers the entire process from inception to the end of an application's life. It comprises several disciplines: project management, requirements management, architecture and design, software development, testing and quality assurance, deployment, maintenance, and decommissioning. In this context, DTs are particularly useful for simulating and modeling the behavior of complex systems, enhancing software applications' understanding, development, and management through their lifecycle. DTs may be used to create detailed models of software systems and the dynamics around them over the changing lifecycle flow. DTs can predict performance, improve maintainability, and evaluate changes before updating and deploying software. For this reason, this paper focuses on exploring the status of adopting the current digital twin paradigm level within industry and how DTs have evolved to unleash the advanced capabilities of the software industry. This is achieved by a deep analysis of the literature with the application of a systematic literature review conducted on Scopus, the Web of Science, and the ScienceDirect database.