25 July 2024Investigation into the use of machine learning to detect memory leaks during continuous integration testing of observatory control software
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During ongoing maintenance and development of software used to control the European Southern Observatory (ESO) Very Large Telescope (VLT), the detection of memory leaks in legacy and newly developed software is of the utmost importance. This paper describes investigations into the development and use of additional test support software using Machine Learning (ML) to determine the presence of memory leaks. The software is implemented to integrate within existing pytest code and is designed to be executed alongside software module nightly tests as part of Continuous Integration (CI) testing. The work’s prime objective is to highlight memory suspicious processes so that memory leaks can be found and fixed before software deployment at the observatory.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Benjamin Carpenter andAlastair Borrowman
"Investigation into the use of machine learning to detect memory leaks during continuous integration testing of observatory control software", Proc. SPIE 13101, Software and Cyberinfrastructure for Astronomy VIII, 131012P (25 July 2024); https://doi.org/10.1117/12.3018392
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Benjamin Carpenter, Alastair Borrowman, "Investigation into the use of machine learning to detect memory leaks during continuous integration testing of observatory control software," Proc. SPIE 13101, Software and Cyberinfrastructure for Astronomy VIII, 131012P (25 July 2024); https://doi.org/10.1117/12.3018392