LEDCity is a Zurich based cleantech start-up and develops an autonomous plug and play lighting system to reduce the energy consumption of lighting by 90% compared to conventional lights. LEDCity’s AI-based optimization of lighting systems (OLS) is a radical new approach to sensor controlled lighting systems. OLS can reduce the energy consumption of lighting through automation. They have a highly effective AI-trained plug and play LED lighting system which is smart, simple and efficient. In each light there are sensors which can adjust light quickly and smoothly. The control unit is decentralized, so there is no need for an expensive management system. This brings challenges to the DevOps process, in both development and testing phases. The ARIES project aims at enhancing LEDCity’s DevOps pipeline, the quality of LEDCity's services, by implementing five main components:

• A real-time data diagnostic component from the operational data of the sensors from the system (e.g., “day-light measurements”, “level of humidity”, etc.);
• A component performing software change analysis to support complexity and risk monitoring in the development process and providing outputs for test engineering;
• A component gathering requirements for enabling testing engineering/generation/automation LEDCity mechanisms and supporting regression testing automation. This component leverages system-relevant changes elicitation approaches to enable its core automation, to reduce testing execution costs in LEDCity;
• A component integrating feedback loop mechanisms to assess the correctness of real-time measurements (e.g., day-light measurements estimators).

The R&D work of ARIES will enable (i) sandboxed execution and analysis of Efficient Energy Service Platforms (EESP) usage's behavior models and (ii) will improve the EESP services reliability as well as LEDCity revenue strategies. This will enable LEDCity to make well-informed decisions before updating or establishing new EESP services.