# Digital Twins in Software Engineering--A Systematic Literature Review and Vision
## 2 Background
### DT Concept and Its Adoption
Privacy/cybersecurity/ethics: Personally identifiable information (PII) data and information deduced from DTs or other sources raise significant concerns among the articles reviewed. For example, [PERSON] et al. [106] comments that developing these systems involving personal data is subject to cybercrime and non-ethical usage opportunities. Lack of a general framework, DT definition, and benefits: [PERSON] [45] reports gaps in the DT definition and the importance of establishing a standard framework for DTs' definition. For instance, [PERSON] [16] introduces variations in the definition of DTs and provides different methods to detect anomalies with DTs, but none are accepted as a general approach. The complexity of systems/products, continuous change: The evolution of customer demands and the need for efficiency result in a changing manufacturing process ecosystem with increasingly complex and fully automated behaviors. As a reference, [PERSON] [81] mentions that the complexity of models is one of the key issues to cover, together with the computational workload due to the variety of data, assets, and components. As [PERSON] [52] considers, DTs can help to tackle this continuous \begin{table}
\begin{tabular}{c c c} \hline \hline
**Area** & **Articles** & **References** \\ \hline Heterogeneous data, harmonization, & 16 & [17, 18, 24, 28, 32, 36, 46, 53, 62, 63, 77, 81, 85, 86, 87, 105] \\ integrations, interoperability & & \\ \hline Preparation and redefinition of human interaction, & 5 & [26, 44, 45, 49, 67] \\ culture acceptance & & \\ \hline Data quality: Incomplete documentation, binary data, & & \\ real historical data on assets/Cold start, dark data, & 16 & [17, 20, 23, 29, 31, 37, 42, 51, 63, 71, 80, 82, 84, 89, 90, 107] \\ siloed info & & \\ \hline Privacy/Cybersecurity/Ethics & 7 & [27, 31, 45, 55, 72, 90, 106] \\ Lack of General Framework, DT definition, & 14 & [16, 34, 35, 39, 41, 45, 54, 61, 68, 72, 79, 92, 98, 101] \\ The complexity of systems/products, & & \\ continuous change & 9 & [32, 41, 50, 52, 59, 64, 81, 91, 93] \\ Lack of real-world applications implemented & 8 & [22, 41, 45, 58, 74, 75, 83, 95] \\ Data infrastructure, talent in data science knowledge, & 12 & [25, 34, 45, 47, 65, 69, 70, 72, 73, 78, 81, 97] \\ high fidelity in mirrored information: & & \\ \hline Lack of more research results & 12 & [19, 21, 57, 66, 68, 88, 94, 96, 100, 102, 103, 104] \\ \hline The complexity of the DT model design & 8 & [30, 38, 40, 48, 56, 76, 81, 99] \\ and interpretation & & \\ \hline Bias, Coding bias & 2 & [14, 60] \\ \hline
**Total analyzed articles** & **94** & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: DT’s main drawbacks in smart manufacturing. change, but to build them, the article recognized a considerable effort in organization architecture, big data solutions, and digital transformation. * Lack of real-world applications: Many works indicate a lack of tested initiatives for long-term real-world scenarios, and existing scenarios have many constraints to verify their efficacy. [PERSON] et al. [41] describe the lack of practice-based frameworks and operational and implementation guidelines in the existing scenarios as a top issue. * Data infrastructure, talent in data science knowledge, and high fidelity in mirrored information: The explosion of big data can pose problems in capturing a sufficient variety of data to mirror physical systems into a DT. Data science can minimize its impact. [PERSON] [69] believes that data science knowledge is a critical capability for industries to implement DTs-related technologies and is a potential constraint. The main reasons are that the infrastructure costs grow remarkably, and the available talent to apply data science remains limited. * Lack of research results: There are insufficient research results in specific areas to compare and build better proposals for setting the basis for DTs. [PERSON] et al. [66] summarize a lack of concerns in specific applicability areas.