Softcom is pioneering in automated testing with the MALT-P (Machine Learning Automated Test Processes) project, which focuses on leveraging automation and machine learning in the domain of Automated Test Generation. This ongoing study investigates and prototypes automation methods for testing facilities at both ESOC and ESTEC.

The project explores use cases such as the automatic generation of test cases using Generative AI models for spacecraft reaction wheels (functional simulation testing) and the application of ML techniques to learn pass/fail criteria for test cases in the GNSS performance monitoring of ESOC’s NAPEOS (Navigation Package for Earth Orbiting Satellites) system.

Softcom is responsible for requirements engineering, prototype development, as well as prototype optimization and bug fixing. Technologies used include GitLab CI/CD pipelines, Matlab/Simulink, Python, and Opensearch.

The MALT-P project underscores Softcom’s commitment to advancing testing processes through innovative solutions, integrating cutting-edge machine learning technologies into the aerospace industry.

End Customer: ESTEC    

Team: etamax Space GmbH, SoftCom-Int, SCI GmbH, National Centre for Scientific Research “Demokritos”

Position: Subcontractor

under a programme of, and funded by, the European Space Agency