Exploiting User Journeys and Testing Automation for Supporting Efficient Energy Service Platforms
Auf einen Blick
- Projektleiter/in : Prabin Joel Jones, Dr. Sebastiano Panichella
- Projektteam : Nicolas Ganz, Florian Gärtner-Wyniger, Gabriela Eugenia Lopez Magaña, Dr. Marcela Ruiz, Soumya Susovita
- Projektstatus : laufend
- Drittmittelgeber : Innosuisse (Innovationsprojekt / Projekt Nr. 45548.1 IP-ICT)
- Projektpartner : Bond Mobility (Europe) AG, LEDCity AG
- Kontaktperson : Sebastiano Panichella
Emerging Mobility As A Services (MAAS) are pushing the boundaries of DevOps practices and processes, with new challenges to handle for both practitioners and researchers. MAAS providers such as BOND (Bike ON Demand start-up in Switzerland) employ DevOps innovations to sustain the evolution of future MAAS services reliability, evolvability, and testability. The rapid and varying BOND users' constraints, requirements, and satisfaction are key and still challenging aspects that are targeted also by other major MAAS service providers. In this context, BOND already provides a Function as a Service (FaaS) framework, which has seen good acceptance by the community in Switzerland. The development of the framework was supported by a previous KTI project. The ARIES project aims at enhancing BOND’s DevOps pipeline, the quality of BOND's services, and its customers' experience, by implementing four main components. The following components implement requirements and DevOps mechanisms, that will be integrated into a modern BOND dashboard:
- A self-adaptive and real-time user-profile analyzer able to monitor user behavioral models;
- A user-oriented interactions analyzer generating dynamic and customized BOND feedback actions, depending on the user needs;
- A user-oriented self-adaptive component gathering user-oriented requirements for enabling testing engineering BOND mechanisms. This component leverages user-relevant changes elicitation approaches to enable its core automation.
- A component integrating dynamic user profile offerings as well as relevant price estimators/simulators are delivered, considering the outcomes of the aforementioned components.
The R&D work of ARIES will enable (i) sandboxed execution and analysis of MAAS user's behavior models and (ii) will improve the MAAS services reliability as well as BOND revenue strategies. This will enable BOND to make well-informed decisions before updating or establishing new MAAS services.
Di Sorbo, Andrea; Visaggio, Corrado A.; Di Penta, Massimiliano; Canfora, Gerardo; Panichella, Sebastiano,
37th International Conference on Software Maintenance and Evolution (ICSME), Luxembourg, 27 September - 1 October 2021.
ZHAW Zürcher Hochschule für Angewandte Wissenschaften.
Verfügbar unter: https://doi.org/10.21256/zhaw-23363