TY - GEN
T1 - PRIMAD
T2 - 2025 IEEE Smart World Congress, SWC 2025
AU - Guinea-Cabrera, Miguel Angel
AU - Holgado-Terriza, Juan Antonio
AU - Pico-Valencia, Pablo A.
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - Current Digital Twin (DT) adoption in the Software Development Lifecycle (SDLC) faces significant hurdles, including a lack of standardized methods, limited research, and isolated use cases. Optimizing value streams is crucial for modern software organizations, which are increasingly characterized by diverse and evolving roles. Implementing a digital twin in this context can significantly enhance the ability to identify opportunities for improvement and explore potential change scenarios. To overcome these adoption barriers, we propose PRIMAD, a progressive methodology for building an SDLC DT that delivers incremental benefits by leveraging existing empirical data from issue-tracking systems, such as JIRA. This approach resolves the "cold start"problem, enabling simulations to emerge directly from real-world behavior. Our methodology integrates a Python-based simulation core with an Akka actor system for modeling issues and resources, enriched by supervised machine learning for context-aware predictions. Preliminary results validating PRIMAD components independently (using pm4py, Akka, and LightGBM) show promising outcomes. This paper details our methodology, presents preliminary component-wise results, and outlines future work.
AB - Current Digital Twin (DT) adoption in the Software Development Lifecycle (SDLC) faces significant hurdles, including a lack of standardized methods, limited research, and isolated use cases. Optimizing value streams is crucial for modern software organizations, which are increasingly characterized by diverse and evolving roles. Implementing a digital twin in this context can significantly enhance the ability to identify opportunities for improvement and explore potential change scenarios. To overcome these adoption barriers, we propose PRIMAD, a progressive methodology for building an SDLC DT that delivers incremental benefits by leveraging existing empirical data from issue-tracking systems, such as JIRA. This approach resolves the "cold start"problem, enabling simulations to emerge directly from real-world behavior. Our methodology integrates a Python-based simulation core with an Akka actor system for modeling issues and resources, enriched by supervised machine learning for context-aware predictions. Preliminary results validating PRIMAD components independently (using pm4py, Akka, and LightGBM) show promising outcomes. This paper details our methodology, presents preliminary component-wise results, and outlines future work.
KW - actor model
KW - digital twin
KW - machine learning
KW - software agents
KW - software engineering
UR - https://www.scopus.com/pages/publications/105035837345
U2 - 10.1109/SWC65939.2025.00261
DO - 10.1109/SWC65939.2025.00261
M3 - Contribución a la conferencia
AN - SCOPUS:105035837345
T3 - Proceedings - 2025 IEEE Smart World Congress, SWC 2025, 2025 IEEE Ubiquitous Intelligence and Computing, Autonomous and Trusted Computing, Digital Twin, Metaverse, Scalable Computing and Communications
SP - 1680
EP - 1685
BT - Proceedings - 2025 IEEE Smart World Congress, SWC 2025, 2025 IEEE Ubiquitous Intelligence and Computing, Autonomous and Trusted Computing, Digital Twin, Metaverse, Scalable Computing and Communications
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 18 August 2025 through 22 August 2025
ER -